About Parloa

Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com, HealthEquity, Allianz, SAP, BarmeniaGothaer, and TUI already deploying Parloa at scale.

About the role:

We are looking for a Technical Project Manager (TPM) to lead medium to high complexity deployments of our Agentic AI / conversational AI platform for enterprise customers. In this role, you’ll own the end-to-end delivery lifecycle—from scoping and planning through go-live and hypercare—working in a pod model alongside a Forward Deployed Engineer (FDE) and an Agent Architect (AA). You will be the primary owner of timeline, scope, risk, governance, and stakeholder communication, ensuring that customers successfully transition to an Agentic OS layer for their enterprise communications.

Areas of ownership:

Project Ownership & Delivery

• Own end-to-end delivery for assigned Agentic AI customer projects (scope, schedule, budget, risk, quality).
• Run the pod (TPM + FDE + Agent Architect) as a single accountable delivery unit.
• Select and apply appropriate delivery methodology (Waterfall, Agile, or Hybrid) based on customer context and risk profile.
• Drive project planning: work breakdown, dependencies, critical path, and resource allocations across internal and partner teams.
• Maintain up-to-date project plan, RAID log (Risks, Assumptions, Issues, Dependencies) and change log.

Customer & Stakeholder Management

• Serve as primary delivery contact for customer project sponsors, IT, and business stakeholders.
• Facilitate recurring governance: status calls, steering committees, UAT readouts, and go-live reviews.
• Translate technical complexity into clear business impacts for executive stakeholders.
• Manage scope and change requests, ensuring commercial and delivery impacts are understood and agreed. 

Cross-Functional & Pod Coordination

• Coordinate with Forward Deployed Engineers on integration readiness, environment setup, performance, and production stability.
• Coordinate with Agent Architects on discovery, agent design, prompt/context strategy, and conversational tuning.
• Collaborate with Sales, Solution Engineering, Product, and Support to ensure a seamless customer journey from sales handoff through BAU operations.
• Where partners are involved (SI/GSI/BPO), manage co-delivery responsibilities, expectations, and escalation paths.

Methodology & Process Discipline

• Plan and facilitate sprints, retros, and sprint reviews for Agile/hybrid projects; lead stage-gate reviews for Waterfall projects.
• Enforce documentation standards (Charter, Design Docs, Runbooks, Handover Docs).
• Ensure risk and compliance requirements (security reviews, data governance, regulatory constraints) are captured and incorporated into the plan.

Metrics & Reporting

• Define and track deployment KPIs, such as:
• Time to Blueprint, Time to First Agent in UAT, Time to Go-Live
• Milestone On-Time Rate
• Defect and incident trends during QA/UAT and Hypercare
• Report delivery status, risk posture, and mitigation plans to internal and external stakeholders.
• Contribute data to capacity and utilization models to inform planning and staffing.

Who you are:

• 5–8+ years of experience in technical project management, program management, or delivery management for SaaS or enterprise software.
• Hands-on experience delivering solutions involving at least one of:
• Contact Center / CCaaS, UCaaS, conversational AI, IVA/IVR, or related CX platforms.
• Demonstrated ability to work across technical and business stakeholders (IT, Security, Operations, Business Owners).
• Solid understanding of software delivery methodologies (Agile/Scrum, Hybrid, and Waterfall) and when to apply each.
• Strong skills in project planning tools (e.g., Jira, Asana, Monday, MS Project, Smartsheet) and collaboration tools (e.g., Confluence, Notion, Miro).
• Excellent communication, facilitation, and conflict resolution skills.
• Proven track record managing multiple concurrent projects with global, distributed teams.
• Fluency in English & German is required

NICE TO HAVE:

• Experience delivering AI/ML, NLP, LLM-based, or Agentic AI solutions.
• Background in professional services or consulting working with enterprise clients.
• Familiarity with APIs, webhooks, event-driven architectures, cloud infra (AWS/Azure/GCP).
• Certifications: PMP, PRINCE2, CSM/CSPO, SAFe, or equivalent.
• Experience with partner co-delivery (SI, GSI, BPO, or ISV ecosystems).

Our Recruiting Process:

Talent Acquisition → Hiring Manager → Technical Interview(s) → Bar Raiser

About Parloa

Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience — one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like IKEA and Booking.com already deploying Parloa at scale.

About the role:

Parloa is building the AI platform that enterprises trust with their most important conversations. That trust starts with security — and we need someone to own it entirely.

As Director of Security, you won't just manage a team. You'll shape how a fast-scaling AI company thinks about security from the ground up: building the strategy, the culture, and the systems that protect our platform, our customers, and the data they entrust to us. You'll lead our SecOps team within Tech Platform, partner across IS&T and Internal IT, and be the person our customers look to when they need confidence that Parloa takes security as seriously as they do.

This is a builder role. If you want to define what security looks like at an AI-native company — not inherit someone else's playbook — this is it.

Areas of ownership:

• Define and execute the product and platform security strategy — building the programs, tooling, and practices that scale with a fast-growing AI platform.
• Lead and grow the security team — hiring exceptional talent, developing senior leaders, and fostering a culture where security is every engineer's responsibility.
• Own application security across the SDLC — embedding secure coding practices, SAST/DAST/SCA tooling, threat modeling, and architectural security reviews into engineering workflows, enabling velocity without compromising safety.
• Design and implement security architectures for cloud-native applications, Kubernetes workloads, and CI/CD pipelines.
• Build and run the detection and response program — developing threat detection capabilities, tuning alerting and telemetry, and leading incident investigations, containment, and postmortems with technical rigor.
• Drive cloud security posture across our AWS/GCP infrastructure, covering identity and access, network segmentation, secrets management, and infrastructure-as-code security.
• Be Parloa's product security voice in customer engagements — fielding security questionnaires, supporting enterprise due diligence, and translating technical posture into customer confidence.
• Advance AI-specific security practices — securing LLM pipelines, model interactions, prompt injection surfaces, and data handling across the platform.
• Collaborate with IS&T on shared boundaries — coordinating on incident escalation, identity systems, and compliance requirements where product and corporate security intersect, without duplicating ownership.
• Establish security metrics that matter — proving that product security posture is measurably improving, not just maintained.

Who you are:

• 12+ years in security, with at least 5 years leading security teams, ideally in product or application security at a SaaS or platform company.
• Deep hands-on experience securing cloud-native, containerized environments (AWS/GCP, Kubernetes) — you can still roll up your sleeves when it matters.
• Strong application security background — you understand secure architecture, common vulnerability classes, and how to shift security left without slowing engineering down.
• Proven detection & response expertise — you've built or significantly improved SOC/detection capabilities and led incident response in production environments.
• Effective communicator across audiences — you can translate product security risk into business terms for executives and customers, and dive into technical detail with engineers.
• Familiarity with compliance frameworks (SOC 2, ISO 27001) as they relate to product security controls — you've contributed to audits and customer security reviews, even if GRC ownership sits elsewhere.
• Genuine curiosity about AI security — experience securing LLM-based systems is a strong plus, but a sharp instinct for emerging risk is what counts.

Our recruiting process:

Recruiter video call → Technical Peer Interview → Expert interview(s) → Bar Raiser

Why Parloa?

We’re at the beginning of a new era in customer experience, one where AI doesn’t just respond, but understands, reasons, and takes action. We’re building agentic AI that enterprises trust with their most important customer moments: complex questions, high volumes, real stakes. When millions of people reach out to a brand, those interactions aren’t just support tickets; they’re defining experiences. We’re here to raise the standard: making every conversation seamless, intelligent, and genuinely helpful. If you care about shaping how businesses and customers connect at scale—and want your work to matter in real, everyday moments—this is where you do it.

At Parloa, ownership isn’t a buzzword; it means being accountable for outcomes, not just tasks. We operate in a category that’s evolving fast, where the bar is high, and the problems are complex. We hire people who think in solutions, communicate with clarity, and follow through. People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.

We’ve grown beyond the earliest startup phase, but we’ve kept the intensity: fast execution, direct feedback, and a strong expectation to contribute meaningfully from day one.

Backed by leading global investors like General Catalyst, EQT Ventures, and Altimeter Capital, we’re scaling with a clear ambition: to become the global category leader in enterprise-grade conversational AI.

What's the opportunity?

Fin is the #1 AI agent for customer service. It resolves over 1M customer conversations every week, and we're just getting started. We're on the cutting edge of defining how AI agents should work — inventing paradigms that didn't exist six months ago and discovering entirely new problem spaces every time we push the technology further.

This role sits at the heart of the Service Agent pillar — 12 teams and growing, building every capability that makes Fin work. You'll partner at the strategic pillar level: broad context across work streams, deep technical understanding of how Fin operates end-to-end, and the autonomy to decide where you need to go next. When we need a new work stream spun up from scratch — a brand new team, an ambiguous problem space, aggressive timelines — you're the person who makes that happen.

The competitive landscape is intense. Fast-moving AI-native startups are coming hard for this space, shipping aggressively and going after the same deals we are. We need someone who thrives under that pressure, who can maintain technical excellence while moving at startup speed within a company that has the resources and customer base to make that speed count.

What will you do?

• Partner at the strategic pillar level. You'll have broad context across work streams and use that to inform technical strategy and investment priorities. What should we care about? Where should we invest? Where do you need to step in? These are your decisions.
• Spin up 0-to-1 work streams. Bring together engineers who've never worked as a team, disambiguate the problem space, build momentum under aggressive timelines, set high expectations, and drive execution.
• Execute on the most ambiguous, highest-stakes problems. You're hands-on. You write code, you ship features, you're deep in the weeds. We've seen too many Principal Engineers who are brilliant but too far from execution to be effective here.
• Lead experimental work at the AI frontier. Run your own A/B tests, do prompt engineering, build evals, and calibrate accuracy, cost, and latency for LLM-powered features. Much of what we're building are new paradigms for customer-facing software.
• Shape long-term technical strategy through execution. Your hands-on work informs the architecture. You're simultaneously building and thinking about what needs to change about how we build products — data models, system design, the shift from GUI-first to agent-first interfaces.
• Work across the full stack in an AI-first development environment. Every engineer has Claude Code with unlimited tokens and a sophisticated ecosystem of MCP servers, skills, and shared context. You'll push the boundaries of what's possible with AI-assisted development and help shape how the entire engineering org works.
• Raise the bar for the people around you. Give direct, actionable feedback that changes outcomes. We don't need someone who mentors juniors on the basics — we need someone who can accelerate and grow the senior and staff engineers around them.

What are we looking for?

The core requirement: engineering depth and product thinking. We need someone who combines deep engineering ability with strong product and design instincts — not one or the other. We don't want engineers who are technically brilliant but disconnected from the product. And we don't want product-minded engineers who have great instincts but can't execute at the level this work demands. You need to be able to both build complex systems AND identify the right problems to solve. This is the single most important dimension we evaluate on.

Beyond that:

• Operated at real scale and has builder energy. You've likely spent time at companies operating at significant scale — the kind of environment where you've seen what large-scale distributed systems actually look like in production. But you also have startup DNA: urgency, resourcefulness, a bias toward building over discussing.
• AI fluency. You're actively experimenting with AI-assisted development, pushing the boundaries of what's possible, and forming opinions about how it changes the way teams build software. This matters more than any specific stack experience.
• Deep technical depth with breadth. You can go deep into the weeds on at least one technical domain, but you also navigate complex multi-team systems with ease. In conversation, you move fluidly from business context to technical strategy to implementation details.
• Communication as a superpower. You can explain to leadership why a technical investment matters, align multiple teams around a complex project, and then walk an engineer through the gnarly implementation details. This role lives and dies on your ability to communicate across every level.
• Extreme autonomy. You partner with the Engineering Director on where you think the pillar needs to go next. You identify problems, form convictions, and act on them. If you're the kind of engineer who waits to be told what to work on, this isn't the right role.
• Critical thinking about the business. You understand what Intercom is optimizing for and translate that into technical decisions. Which work streams need investment? Where are we falling behind? What should we defund to go harder somewhere else? You think at this level naturally.
• At least ~10+ years of experience, with significant time as a technical leader driving complex projects across multiple teams and stakeholders.
• Stack agnostic. We use Ruby on Rails, React (migrating from Ember), and AWS. But AI tools increasingly abstract the stack — what matters is your ability to learn, build, and operate effectively with tools like Claude Code. If you're great with a high-level programming language and fluent with AI-assisted development, you'll be productive fast.

Why Intercom, why now?

Fin is #1 — and the race is heating up. We built the leading AI agent for customer service, but fast-moving AI-native startups are coming for this space. This is one of the most consequential competitive moments in tech history, and the engineering decisions made in the next 12 months will determine who wins.

A rare pivot that actually worked. Intercom has been building for 15 years — deep product, massive customer base, strong revenue. But unlike most legacy SaaS companies, we made a successful pivot to AI. While most of SaaS trends down, Intercom trends up. We recently raised $250M to go even further even faster..

AI-first engineering culture. Every engineer gets Claude Code with unlimited Opus 4.6 tokens and 1M context. We're investing heavily in the platform capabilities — MCP servers, skills, shared tooling — that make AI-assisted development genuinely transformative. You'll be at the sharp edge of figuring out what this means for how teams work.

The sweet spot. Big enough to have real resources, customers, and revenue. Small enough that you have genuine agency. Bold enough to make decisions that most companies of this size wouldn't. You come to Intercom if you want the impact of a startup with the backing of a company that's been winning for 15 years.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too 
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid

What's the opportunity?

Fin is the #1 AI agent for customer service. It resolves over 1M customer conversations every week, and we're just getting started. We're on the cutting edge of defining how AI agents should work — inventing paradigms that didn't exist six months ago and discovering entirely new problem spaces every time we push the technology further.

This role sits at the heart of the Service Agent pillar — 12 teams and growing, building every capability that makes Fin work. You'll partner at the strategic pillar level: broad context across work streams, deep technical understanding of how Fin operates end-to-end, and the autonomy to decide where you need to go next. When we need a new work stream spun up from scratch — a brand new team, an ambiguous problem space, aggressive timelines — you're the person who makes that happen.

The competitive landscape is intense. Fast-moving AI-native startups are coming hard for this space, shipping aggressively and going after the same deals we are. We need someone who thrives under that pressure, who can maintain technical excellence while moving at startup speed within a company that has the resources and customer base to make that speed count.

What will you do?

• Partner at the strategic pillar level. You'll have broad context across work streams and use that to inform technical strategy and investment priorities. What should we care about? Where should we invest? Where do you need to step in? These are your decisions.
• Spin up 0-to-1 work streams. Bring together engineers who've never worked as a team, disambiguate the problem space, build momentum under aggressive timelines, set high expectations, and drive execution.
• Execute on the most ambiguous, highest-stakes problems. You're hands-on. You write code, you ship features, you're deep in the weeds. We've seen too many Principal Engineers who are brilliant but too far from execution to be effective here.
• Lead experimental work at the AI frontier. Run your own A/B tests, do prompt engineering, build evals, and calibrate accuracy, cost, and latency for LLM-powered features. Much of what we're building are new paradigms for customer-facing software.
• Shape long-term technical strategy through execution. Your hands-on work informs the architecture. You're simultaneously building and thinking about what needs to change about how we build products — data models, system design, the shift from GUI-first to agent-first interfaces.
• Work across the full stack in an AI-first development environment. Every engineer has Claude Code with unlimited tokens and a sophisticated ecosystem of MCP servers, skills, and shared context. You'll push the boundaries of what's possible with AI-assisted development and help shape how the entire engineering org works.
• Raise the bar for the people around you. Give direct, actionable feedback that changes outcomes. We don't need someone who mentors juniors on the basics — we need someone who can accelerate and grow the senior and staff engineers around them.

What are we looking for?

The core requirement: engineering depth and product thinking. We need someone who combines deep engineering ability with strong product and design instincts — not one or the other. We don't want engineers who are technically brilliant but disconnected from the product. And we don't want product-minded engineers who have great instincts but can't execute at the level this work demands. You need to be able to both build complex systems AND identify the right problems to solve. This is the single most important dimension we evaluate on.

Beyond that:

• Operated at real scale and has builder energy. You've likely spent time at companies operating at significant scale — the kind of environment where you've seen what large-scale distributed systems actually look like in production. But you also have startup DNA: urgency, resourcefulness, a bias toward building over discussing.
• AI fluency. You're actively experimenting with AI-assisted development, pushing the boundaries of what's possible, and forming opinions about how it changes the way teams build software. This matters more than any specific stack experience.
• Deep technical depth with breadth. You can go deep into the weeds on at least one technical domain, but you also navigate complex multi-team systems with ease. In conversation, you move fluidly from business context to technical strategy to implementation details.
• Communication as a superpower. You can explain to leadership why a technical investment matters, align multiple teams around a complex project, and then walk an engineer through the gnarly implementation details. This role lives and dies on your ability to communicate across every level.
• Extreme autonomy. You partner with the Engineering Director on where you think the pillar needs to go next. You identify problems, form convictions, and act on them. If you're the kind of engineer who waits to be told what to work on, this isn't the right role.
• Critical thinking about the business. You understand what Intercom is optimizing for and translate that into technical decisions. Which work streams need investment? Where are we falling behind? What should we defund to go harder somewhere else? You think at this level naturally.
• At least ~10+ years of experience, with significant time as a technical leader driving complex projects across multiple teams and stakeholders.
• Stack agnostic. We use Ruby on Rails, React (migrating from Ember), and AWS. But AI tools increasingly abstract the stack — what matters is your ability to learn, build, and operate effectively with tools like Claude Code. If you're great with a high-level programming language and fluent with AI-assisted development, you'll be productive fast.

Why Intercom, why now?

Fin is #1 — and the race is heating up. We built the leading AI agent for customer service, but fast-moving AI-native startups are coming for this space. This is one of the most consequential competitive moments in tech history, and the engineering decisions made in the next 12 months will determine who wins.

A rare pivot that actually worked. Intercom has been building for 15 years — deep product, massive customer base, strong revenue. But unlike most legacy SaaS companies, we made a successful pivot to AI. While most of SaaS trends down, Intercom trends up. We recently raised $250M to go even further even faster..

AI-first engineering culture. Every engineer gets Claude Code with unlimited Opus 4.6 tokens and 1M context. We're investing heavily in the platform capabilities — MCP servers, skills, shared tooling — that make AI-assisted development genuinely transformative. You'll be at the sharp edge of figuring out what this means for how teams work.

The sweet spot. Big enough to have real resources, customers, and revenue. Small enough that you have genuine agency. Bold enough to make decisions that most companies of this size wouldn't. You come to Intercom if you want the impact of a startup with the backing of a company that's been winning for 15 years.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too 
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid

Team

The AI Enablement team supports the adoption of AI capabilities across Raisin’s products and internal systems.

The team focuses on establishing a consistent and reliable approach to using AI in production by providing shared foundations, guidance, and reusable solutions. This includes working with engineering teams to integrate AI into existing systems, ensuring solutions are scalable, maintainable, and aligned with security and regulatory requirements.

The scope ranges from improving internal workflows to enabling new product capabilities, depending on where AI can deliver measurable value.

Your Responsibilities

As an AI Engineer, you will help shape how AI is applied across the organization by combining hands-on engineering with enablement and guidance.

Enable teams to adopt AI effectively

• Work with product and engineering teams to identify and refine AI use cases
  
  
• Support teams in integrating AI capabilities into their systems and workflows by providing clear guidance on patterns, trade-offs, and practical implementation approaches
  
  
• Promote consistent and reusable solutions to reduce fragmentation across teams

Contribute to shared capabilities where needed

• Develop or extend shared components, integrations, or services when required to unblock teams
  
  
• Define and implement patterns for integrating LLM capabilities into the platform
  
  
• Contribute to shared components or lightweight services that simplify access to AI capabilities across teams
  
  
• Manage AI costs and budget usage when providing shared model access and services to other teams.
  
  
• Support teams in developing retrieval-based approaches that make internal knowledge accessible to AI-driven use cases

Guide technical decisions

• Evaluate tools, platforms, and approaches with consideration for scalability, security, and maintainability
  
  
• Help teams choose appropriate solutions based on context rather than applying AI by default
  
  
• Contribute to establishing a pragmatic and consistent approach to AI usage

Ensure robustness and responsible usage

• Optimize AI system efficiency by making principled model selections based on cost-performance trade-offs, inference latency, and token usage optimization
  
  
• Support the implementation of safeguards such as access controls, validation, and monitoring
  
  
• Collaborate with Security, Legal, and other stakeholders on requirements related to AI usage
  
  
• Contribute to practices that ensure solutions are reliable and aligned with internal standards

Your Profile

Background

• Strong experience as a Software Engineer (5+ years), with focus on backend or platform development
  
  
• Strong generalist mindset, comfortable working across technical domains
  
  
• Experience working on production systems and collaborating across teams
  
  
• Comfortable operating in environments with evolving scope and priorities

Technical Skills

• Proficiency in at least one programming language (Python preferred; JVM languages also relevant)
  
  
• Experience with cloud environments (AWS preferred)
  
  
• Solid understanding of APIs, system integration, and distributed systems

AI Experience

• Practical experience working with AI tools or platforms (e.g. LLM APIs or similar technologies)
  
  
• Familiarity with concepts such as prompt design, context engineering, retrieval-based approaches, or AI-driven workflows
  
  
• Ability to assess where AI adds value and where it does not

Join our mission, join our team – and grow with us!

At Raisin, we care about each other and it is one of our top priorities to foster an open and caring environment in which everyone feels welcome and comfortable. Our culture is strongly driven by our ambitious team, which connects more than 75 different nationalities.

As part of our team, you will benefit from:

• Employee Development Budget of €2,000 and four full training days per year.
• Flexible working hours, home office and 30 vacation days.
• A company pension scheme (Betriebliche Altersvorsorge), which we support with 20%.
• Enjoy more than 50+ different sports with Urban Sports Club: We subsidize your membership with more than €20 per month. 
• Do you miss being in the office? The Deutschland Ticket gets you there, which we subsidize with €25 per month.
• Love cycling? With JobRad, lease the bike of your choice and enjoy tax savings, plus Raisin covers your monthly insurance costs.
• Hungry all the time? Snacks, daily fresh fruit as well as drinks provided at the office.
• You are moving from another country or city to join us? We may support your relocation.

We're looking for a Staff Software Engineer with deep experience in AI/ML to join the Notebooks team. Datadog Notebooks provide a flexible tool for ad-hoc data analysis, collaborative investigations, and technical documentation. Its fluidity makes it an ideal surface for agents and humans to interact. The Notebook team is building AI-driven workflows for natural-language data analysis, assisted graph generation, automated investigations, and more. You will act as a technical leader in this domain, focused on building industry-leader AI-powered features. We’re looking for a product-minded ML engineer with strong technical expertise, excellent communication skills, and a track record of driving impactful initiatives end to end.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Spearhead AI initiatives within Notebooks, serving as the technical owner for the design, development, and deployment of ML/AI-driven features.
• Shape the long-term technical direction and strategy for AI workflows within Notebooks and related products.
• Champion the full development cycle: from rapid prototyping and defining success metrics/evaluations to running experiments, iterating, and achieving production-ready scale and reliability.
• Drive product direction with a strong user focus, always advocating for the best end-user experience.
• Coordinate with other leaders and product teams interested in leveraging Notebooks in their AI-powered workflows.
• Develop robust agent systems, including tools, retrieval and planning strategies, guardrails, prompt management/evaluations, fallbacks, and human-in-the-loop processes.
• Stay up to date with industry trends, guiding the adoption of new technologies and evolving best-practices.

Who You Are:

• You have a BS/MS/PhD in a scientific field or equivalent experience
• 10+ years of relevant engineering experience, as well as experience acting as a technical lead
• Proven track record of leading large-scale AI/ML initiatives in a product-driven environment
• You have significant experience in model deployment, development, training, fine-tuning, and evaluation
• You are passionate about mentoring others and cultivating a high-performing engineering culture.
• You’re excited about leveraging AI tools to enhance how you code, solve problems, and build

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Generous and competitive benefits package
• New hire stock equity (RSUs) and employee stock purchase plan
• Continuous career development and pathing opportunities
• Employee-focused best in class onboarding
• Internal mentor and cross-departmental buddy program
• Friendly and inclusive workplace culture
  
  

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Remote

We’re building a new team building to bring the power of Datadog to developers within AI-focused Developer workflows such as AI agents like Cursor, Claude code, GitHub Copilot and Datadog’s native AI agents.

We’re looking for product-minded engineers who are excited about developer tools, SDLC improvements and AI in general to help us quickly define and ship features and tools that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What You’ll Do: 

• Work on agents, prompts, evals, MCPs and direct agent tools from ideation to productization, for allowing Datadog to help companies and developers to maximize the benefit of their integration with Datadog
• Help conceptualize and build innovative User flows and experiences within Datadog’s user space and different extensions 
• Prototype and see what works well in the real world, iterate closely with our users, and then ship
• Follow the latest research on LLM prompt optimization, agents, and code editing.
• Support other Datadog product teams to help them build and productionize their developer-focused AI Workflows. For example, you’ll work with the Live Debugger team to create an agentic production debugging experience, delivering on the vision of  “one click to reproduce” for production errors.

Who You Are: 

• You’re a backend engineer with at least 5 years of professional experience who values code simplicity and performance. You’ve shipped software that uses LLMs to production
• You are passionate about Generative AI and its ability to help software engineers be more productive
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution
• You want to work in a fast, high-growth startup environment that respects its engineers and customers
• You have experience writing classic or AI oriented developer tools(such as IDEs, static code analysis, SDLC tools etc).
• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are now building services and agentic tools that provide AI coding agents and end users with deeper context about codebases. Our code retrieval service already delivers meaningful improvements in agent speed and task performance, and we aim to push this further – extracting richer insights than snippets alone at the scale of several hundred thousand repositories. 

We are looking for an AI Engineer who can design and implement agentic tools from scratch, bring them to the end users, and make coding with agents smarter, faster, and more reliable.

In this role, you will:

• Translate product needs into agentic tools and services and ship them to production.
• Scale prototypes to large numbers of users.
• Improve and test our semantic search service.
• Build evaluation pipelines for code retrieval and coding task performance.

We’ll be happy to have you on our team if you have:

• At least five years of hands-on NLP experience.
• Experience in information retrieval systems.
• Strong experience in LLM prompt and tool engineering, as well as with AI agents.
• Solid programming skills in Python and/or Kotlin (and the willingness to learn the language you’re unfamiliar with).
• Experience building and operating production-grade software systems.

We’d be especially thrilled if you have:

• Experience developing in Kotlin or Java.
• Previously worked with code intelligence (e.g. AST, symbol graphs).

#LI-DNI

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning. 

Spectrum collects data from an organization’s code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that’s extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

Spectrum is a resident of JetBrains' startup incubator, with startup speed and autonomy, and backed by 25 years of developer tooling expertise. We are looking for a top-class ML Engineer who will help us shape the future of software development. You will own our AI and ML engineering stack and help define the research agenda for our team. Your technical vision and design decisions will directly shape the product and determine its success.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
• Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
• Establishing MLOps practices, including orchestration, observability, and experiment tracking.
• Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
• Defining hiring criteria, growing the ML team, and shaping the ML team culture.

We expect you to have:

• A proven track record as an ML/AI Lead.
• At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
• Vector databases and retrieval-augmented generation (RAG) patterns.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal. 

Our ideal candidate would have:

• Experience with ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• The ability to think strategically about product-led AI, beyond just training models in isolation.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.

#LI-KP1

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior AI/ML Engineer to build and evolve the ML-powered systems at the heart of our product.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction and retrieval, and subsequent reasoning.
• Creating datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving AI agents, which will involve prompt engineering, model selection, and inference optimization.
• Contributing to our MLOps practices, including orchestration, observability, and experiment tracking.

We expect you to have:

• At least four years of experience building ML/AI systems in production environments, with hands-on LLM experience.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangGraph, Pydantic AI, or an equivalent.
• Retrieval-augmented generation (RAG) patterns and vector databases.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• Opportunities to publish research at top venues in collaboration with JetBrains Research.

#LI-DNI

So, what’s the role all about?

A NiCE AI Forward Deployed Engineer is a highly technical full-stack engineer responsible for designing, building, and deploying AI-driven customer engagement solutions using the NiCE digital and AI portfolio. This role sits at the intersection of software engineering, AI agent development, and customer solution architecture. The engineer works directly with customers and internal stakeholders to translate complex business challenges into intelligent automation solutions across voice and digital channels. The AI Forward Deployed Engineer will design, prototype, and operationalize AI agents that integrate with enterprise systems and deliver scalable customer self-service experiences powered by intelligent virtual agents, knowledge management, and omnichannel engagement capabilities. This role requires deep technical expertise across full-stack development, AI agent architectures, APIs, integrations, and conversational automation, combined with the ability to collaborate directly with customers and business leaders.

How will you make an impact?  

• AI Agent Design & Development
  • Design and build AI-driven virtual agents that automate customer service workflows across digital and voice channels.
  • Develop intelligent automation using the NiCE AI and Digital portfolio, including conversational AI, knowledge systems, and omnichannel engagement capabilities.
  • Architect agent behaviors including intent handling, workflow orchestration, and multi-system interactions.
  • Continuously refine and evolve agents through iterative testing, model tuning, and performance optimization.
• AI Agent Lifecycle Ownership
  • Own the full lifecycle of AI agents from initial design through deployment and ongoing optimization.
  • Define agent architecture including prompting strategies, knowledge retrieval patterns, and decision logic.
  • Implement observability, evaluation, and feedback loops to improve agent accuracy, reliability, and business outcomes.
  • Monitor agent performance and evolve agents to handle increasingly complex customer interactions.
• System Integration & Automation
  • Build integrations between AI agents and enterprise systems including CRM, commerce platforms, knowledge bases, and operational systems.
  • Develop APIs, services, and orchestration layers enabling agents to perform real business transactions.
  • Implement multi-step workflows that coordinate API calls across multiple systems of record.
  • Ensure secure, scalable, and reliable integrations across customer environments.
• Full Stack Solution Development
  • Build full-stack solutions that support agent interactions including web interfaces, backend services, and integration layers.
  • Develop prototypes and working applications that demonstrate end-to-end AI automation scenarios.
  • Create reusable components and frameworks that accelerate development of new AI agent capabilities.
  • Maintain and enhance demo environments and development environments used for solution innovation.
• Customer Solution Engineering
  • Partner directly with customers to understand operational challenges and identify opportunities for AI-driven automation.
  • Translate business requirements into technical architectures and working solutions.
  • Build prototypes, proof-of-value implementations, and production-ready agent capabilities.
  • Act as a trusted technical advisor to both business and technology stakeholders.
• Product Innovation & Feedback
  • Collaborate with product, engineering, and sales teams to identify product gaps and innovation opportunities.
  • Prototype new capabilities that extend the NiCE AI platform.
  • Provide direct field insights that inform product roadmap and future platform capabilities.
  • Contribute reusable patterns, integrations, and architectures that improve future deployments.
• Technical Demonstration & Enablement
  • Design and build advanced technical demonstrations showcasing AI-driven customer experiences.
  • Develop reusable demo frameworks and environments that illustrate enterprise use cases.
  • Support sales engineering and presales teams in communicating the value of AI-driven customer engagement solutions.
  • Enable internal teams with technical knowledge of AI agents, integrations, and platform capabilities.

Have you got what it takes?

• The AI Agent Engineer will work across several core technical domains:
  AI & Agent Systems
  • Large Language Models
  • Prompt engineering
  • Agent orchestration frameworks
  • Retrieval-augmented knowledge systems
  • Conversational AI architecture
  Full Stack Development
  • Backend services and APIs
  • Web and conversational interfaces
  • Integration layers
  • Data pipelines
  Enterprise Integrations
  • REST APIs and microservices
  • CRM and business system integrations
  • Knowledge systems and data stores
  • Identity and access management

What’s in it for you?

Join an ever-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!

Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman is Sebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform, and co-founder, Tim Shi, an early member of Open AI.

Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's at Cresta.

About the role:

At Cresta, the AI Agent team is on a mission to create state-of-the-art AI Agents that solve practical problems for our customers. We are focused on leveraging the latest technologies in Large Language Models (LLMs) and AI Agent systems, while ensuring that the solutions we develop are cost-effective, secure, and reliable. 

As an AI Agent Engineer, you'll be at the forefront of deploying AI agents that address real-world challenges. In this role, you will work closely with customers as well as our software and machine learning engineers, ensuring high-impact AI Agent deployments and contributing to the continuous improvement of our core AI platform. You’ll develop intelligent AI agents, integrate them seamlessly with external systems and offer hands-on technical expertise to ensure successful deployments.. This position requires strong engineering skills, adaptability, and customer engagement. If you are self-driven, analytical, and eager to leverage AI in practical applications, this role is for you.

What You'll Do :

• AI Agent Development: Develop, configure, deploy, and optimize AI agents using Cresta’s AI platform and tools.
• System Integration: Build AI agent integrations with external systems (APIs, databases, CRMs, etc.) to ensure seamless workflow integration.
• Performance Optimization: Optimize AI agent performance (e.g. fine-tune prompts and configurations) and troubleshoot issues in complex enterprise environments.
• Requirements Gathering: Collaborate with customers and internal stakeholders to gather technical requirements and translate business needs into AI Agent solutions.
• Demos & Iteration: Conduct interactive demos and present compelling proof-of-concepts to prospective customers, proactively gather feedback, and iteratively refine solutions to meet objectives.
• Project Coordination: Define project milestones, create implementation plans, and coordinate execution with internal teams to ensure on-time delivery. Provide a tight feedback loop to our product and engineering teams — identifying gaps, building custom tooling, and influencing the roadmap through real-world deployment learnings.
• Partner Cross-Functionally: Collaborate with PMs to define agent goals, iterate rapidly based on customer feedback, and shape product capabilities that maximize customer ROI.
• Technical Guidance: Serve as a trusted technical advisor for the customer, guiding best practices for AI agent adoption and usage. Provide technical guidance on AI agent best practices, including architecture design, security considerations, and scalability planning.

What We're Looking For:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience: 3+ years of experience of full time working experience in software development/ consulting, AI/ML engineering, or system integration, or as FDE
• Programming Skills: Proficiency in Python and Golang, with the ability to write clean, efficient code.
• AI/ML Knowledge: Familiarity with AI/ML concepts. Hands-on experience with large language models (LLMs), and prompt engineering techniques are strongly preferred.
• AI Agent Frameworks: Strong understanding of general AI agent frameworks, function calling, and retrieval-augmented generation (RAG). Hands-on experience of building such a system is strongly preferred.
• Cloud & DevOps: Experience with cloud platforms (AWS, GCP, or Azure) and DevOps practices (CI/CD, containerization, monitoring).
• Integration Expertise: Hands-on experience with integrating systems via APIs, webhooks, and data pipelines.
• Communication: Excellent communication and project management skills.
• Analytical Approach: Ability to use data-driven decision-making, including A/B testing and performance monitoring, to refine solutions.
• Collaborative Builder: You thrive in cross-functional environments, working hand-in-hand with PMs and engineers to turn real customer problems into scalable AI solutions.

Compensation for this position includes a base salary, equity, and a variety of benefits. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.

This posting will be used to fill a newly-created role.

We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. Any outreach claiming to be from Cresta via other sources should be ignored.  If you are uncertain whether you have been contacted by an official Cresta employee, reach out to recruiting@cresta.ai

What's the opportunity?

Fin is the #1 AI agent for customer service. It resolves over 1M customer conversations every week, and we're just getting started. We're on the cutting edge of defining how AI agents should work — inventing paradigms that didn't exist six months ago and discovering entirely new problem spaces every time we push the technology further.

This role sits at the heart of the Service Agent pillar — 12 teams and growing, building every capability that makes Fin work. You'll partner at the strategic pillar level: broad context across work streams, deep technical understanding of how Fin operates end-to-end, and the autonomy to decide where you need to go next. When we need a new work stream spun up from scratch — a brand new team, an ambiguous problem space, aggressive timelines — you're the person who makes that happen.

The competitive landscape is intense. Fast-moving AI-native startups are coming hard for this space, shipping aggressively and going after the same deals we are. We need someone who thrives under that pressure, who can maintain technical excellence while moving at startup speed within a company that has the resources and customer base to make that speed count.

What will you do?

• Partner at the strategic pillar level. You'll have broad context across work streams and use that to inform technical strategy and investment priorities. What should we care about? Where should we invest? Where do you need to step in? These are your decisions.
• Spin up 0-to-1 work streams. Bring together engineers who've never worked as a team, disambiguate the problem space, build momentum under aggressive timelines, set high expectations, and drive execution.
• Execute on the most ambiguous, highest-stakes problems. You're hands-on. You write code, you ship features, you're deep in the weeds. We've seen too many Principal Engineers who are brilliant but too far from execution to be effective here.
• Lead experimental work at the AI frontier. Run your own A/B tests, do prompt engineering, build evals, and calibrate accuracy, cost, and latency for LLM-powered features. Much of what we're building are new paradigms for customer-facing software.
• Shape long-term technical strategy through execution. Your hands-on work informs the architecture. You're simultaneously building and thinking about what needs to change about how we build products — data models, system design, the shift from GUI-first to agent-first interfaces.
• Work across the full stack in an AI-first development environment. Every engineer has Claude Code with unlimited tokens and a sophisticated ecosystem of MCP servers, skills, and shared context. You'll push the boundaries of what's possible with AI-assisted development and help shape how the entire engineering org works.
• Raise the bar for the people around you. Give direct, actionable feedback that changes outcomes. We don't need someone who mentors juniors on the basics — we need someone who can accelerate and grow the senior and staff engineers around them.

What are we looking for?

The core requirement: engineering depth and product thinking. We need someone who combines deep engineering ability with strong product and design instincts — not one or the other. We don't want engineers who are technically brilliant but disconnected from the product. And we don't want product-minded engineers who have great instincts but can't execute at the level this work demands. You need to be able to both build complex systems AND identify the right problems to solve. This is the single most important dimension we evaluate on.

Beyond that:

• Operated at real scale and has builder energy. You've likely spent time at companies operating at significant scale — the kind of environment where you've seen what large-scale distributed systems actually look like in production. But you also have startup DNA: urgency, resourcefulness, a bias toward building over discussing.
• AI fluency. You're actively experimenting with AI-assisted development, pushing the boundaries of what's possible, and forming opinions about how it changes the way teams build software. This matters more than any specific stack experience.
• Deep technical depth with breadth. You can go deep into the weeds on at least one technical domain, but you also navigate complex multi-team systems with ease. In conversation, you move fluidly from business context to technical strategy to implementation details.
• Communication as a superpower. You can explain to leadership why a technical investment matters, align multiple teams around a complex project, and then walk an engineer through the gnarly implementation details. This role lives and dies on your ability to communicate across every level.
• Extreme autonomy. You partner with the Engineering Director on where you think the pillar needs to go next. You identify problems, form convictions, and act on them. If you're the kind of engineer who waits to be told what to work on, this isn't the right role.
• Critical thinking about the business. You understand what Intercom is optimizing for and translate that into technical decisions. Which work streams need investment? Where are we falling behind? What should we defund to go harder somewhere else? You think at this level naturally.
• At least ~10+ years of experience, with significant time as a technical leader driving complex projects across multiple teams and stakeholders.
• Stack agnostic. We use Ruby on Rails, React (migrating from Ember), and AWS. But AI tools increasingly abstract the stack — what matters is your ability to learn, build, and operate effectively with tools like Claude Code. If you're great with a high-level programming language and fluent with AI-assisted development, you'll be productive fast.

Why Intercom, why now?

Fin is #1 — and the race is heating up. We built the leading AI agent for customer service, but fast-moving AI-native startups are coming for this space. This is one of the most consequential competitive moments in tech history, and the engineering decisions made in the next 12 months will determine who wins.

A rare pivot that actually worked. Intercom has been building for 15 years — deep product, massive customer base, strong revenue. But unlike most legacy SaaS companies, we made a successful pivot to AI. While most of SaaS trends down, Intercom trends up. We recently raised $250M to go even further even faster..

AI-first engineering culture. Every engineer gets Claude Code with unlimited Opus 4.6 tokens and 1M context. We're investing heavily in the platform capabilities — MCP servers, skills, shared tooling — that make AI-assisted development genuinely transformative. You'll be at the sharp edge of figuring out what this means for how teams work.

The sweet spot. Big enough to have real resources, customers, and revenue. Small enough that you have genuine agency. Bold enough to make decisions that most companies of this size wouldn't. You come to Intercom if you want the impact of a startup with the backing of a company that's been winning for 15 years.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary, annual bonus and equity
• Regular compensation reviews - we reward great work!
• Generous paid time off above statutory minimum
• Hybrid working
• MacBooks are our standard, but we also offer Windows for certain roles when needed.
• Fun events for employees, friends and family!

#LI-Hybrid

Wir sind mehr als 2.000 Kolleg:innen in 22 Designstudios auf vier Kontinenten – ein hochgradig kollaboratives Team aus Expert:innen für Produkterlebnisse, digitales Marketing und immersive Technologien. Unsere Lösungen entstehen dort, wo Kreativität und Technologie zusammentreffen – mit einem klaren Fokus auf die Bedürfnisse und Erlebnisse der Nutzer:innen. Ob Augmented Reality, Digital Twins, KI-Ökosysteme, Omnichannel-Commerce oder integrierte Kampagnen: Wir schaffen Verbindungen zwischen Menschen und Marken – im B2B- ebenso wie im B2C-Bereich. 

Als Teil des globalen Technologieinnovators Infosys verfügen wir über die nötige Skalierung und Expertise, um große Ideen Wirklichkeit werden zu lassen – und sie weltweit umzusetzen. 

Wir sind eine Motion Picture & 3D Production mit Fokus auf hochwertige CGI- und Content-Produktion für starke Marken (u. a. Automotive, Fashion, Consumer Goods). Unser Sweet Spot: kreative Exploration mit GenAI – und wenn nötig kampagnenfähige, pixel-perfect Ergebnisse im Produktionsmaßstab. 

Deine Rolle 

Du bringst GenAI-Bildgenerierung und -Bildbearbeitung (v. a. ComfyUI) so in unsere CGI-/Compositing-Pipeline, dass aus Ideen schnell realistische, konsistente und markentaugliche Visuals werden. 

Das erwartet Dich 

1.) Look Development & Concept Exploration  

• Du entwickelst Bildwelten, Motive und Variationen für Kampagnen  
• Du arbeitest mit ComfyUI in bestehenden Graphen, pflegst sie und erweiterst sie für konkrete Use Cases (z. B. Environment/Background, Produktintegration, Serien-Varianten). 
• Du baust Prompt-Frameworks / Prompt-Baupläne, um Konsistenz und Qualität über Serien zu halten. 

2.) CGI Enhancement & “AI als Finish”  

• Du nutzt GenAI als Finish-Layer nach dem 3D: Enhancement, Compositing-Unterstützung, Details, Realismus, Konsistenz. 
• Du arbeitest eng mit 3D/CGI/Compositing zusammen (u. a. C4D, Maya, Unreal, Nuke, After Effects, Photoshop, Substance). 
• Du hilfst, typische Produktionsherausforderungen zu lösen (z. B. Produktgenauigkeit, Proportionen, Detailgrad). 

3.) Workflow-Qualität & Skalierung  

• Du setzt auf Batch/Automation in ComfyUI (Queues, Varianten, reproduzierbare Runs). 
• Du evaluierst Modelle/Ansätze (Qualität/Speed) und setzt sie passend zum Use Case ein 
• Du dokumentierst ComfyUI-Templates/Workflows und erstellst Best-Practice Guides, damit Teams sauber weiterarbeiten können. 
• Für kleine Automationen nutzt du gelegentlich Skripte/Helpers (Python/JS). Für größere Infrastruktur-/Engineering-Themen gibt es bei uns separate Engineering-Unterstützung. 

Das bringst Du mit 

• Hands-on Erfahrung mit GenAI im Imaging-Kontext, inklusive Bildgenerierung sowie gezielter Bearbeitung und Qualitätsverbesserung 
• Sichere Praxis mit ComfyUI, insbesondere node-basierte Workflows, saubere Graphen und Troubleshooting 
• Ausgeprägtes Verständnis von Bildgestaltung und Visual Quality, z. B. Komposition, Licht, Materialität und Stil 
• Erfahrung oder fundiertes Verständnis von CGI- und Compositing-Pipelines, inklusive Übergaben, Render-Logik und dem Weg vom Setup zum finalen Bild 
• Portfolio oder Arbeitsproben, die Realismus, Konsistenz und visuelle Qualität unter produktionsnahen Constraints belegen (über reine Prompt-Experimente hinaus) 
• Fließende Deutsch- und/oder Englischkenntnisse (mindestens B2-C1) sind Voraussetzung.

Was zusätzlich begeistert (Bonus, aber kein Muss)

• Git-Grundlagen (Versioning von Workflows/Configs) 
• Kleine Skripting-Skills (Python/JS) für Helpers 
• Erfahrung mit API-basierten Tools (Cloud) und/oder lokalen Setups (Compliance) 
• Automotive/Fashion/Consumer Goods Erfahrung (z. B. Produktinszenierung, Brand-Look, Konsistenz) 
• 3D Dataprep Know-how (CAD cleanup, UV/Material-Prep, Lighting Presets, AOVs/Depth) 

Wie wir arbeiten 

• Du arbeitest mit einem interdisziplinären Team (Creative Direction, CGI/3D, Motion/Comp, Engineering). 
• Typischer Review: 2 Feedbackschleifen (intern/Lead + Freigabe). 
• Qualität ist uns wichtig: kampagnenfähig / photoreal / pixel-perfect, aber wir unterscheiden klar zwischen Exploration und Final. 
• Primär remote innerhalb Deutschlands, mit ca. 2 Tagen/Monat vor Ort in Stuttgart. 
• Alltagssprache im Team ist überwiegend Deutsch – du musst nicht perfekt sein, aber kommunikationsfähig (Englisch geht selbstverständlich). 

Unsere lokalen Benefits 

• Flexible Arbeitszeiten  
• Arbeiten von wo es für dich passt. Zu Hause oder im Büro, ganz nach deinem Geschmack 
• Arbeit für starke Marken und Kund:innen, an die wir glauben  
• Zuschuss beim Urban Sports Club 
• Mitarbeitervorteile & Rabatte über Corporate Benefits 
• aufgeschlossene & nette Menschen in allen Teams 

Klingt nach Dir? Dann schnapp Dir Deinen Laptop und bewirb Dich – wir freuen uns, Dich auf unserer kreativen Reise dabei zu haben! Bitte sende uns: 

• CV  
• Portfolio/Showreel (sehr gern mit 2–3 Case Slides) 
• Optional sehr hilfreich: 1 Beispiel-Workflow (Screenshot/Export deines ComfyUI-Graphs + kurze Erklärung: Input → Schritte → Output → wie du Konsistenz & Produktgenauigkeit sicherst) 

Wir freuen uns besonders über Beispiele, in denen du Produkt-Treue, Proportionen und Realismus unter Constraints gelöst hast.  

Für uns zählt allein Deine Persönlichkeit und Deine beruflichen Fähigkeiten. Dein Geschlecht, Alter oder Aussehen, Deine Herkunft oder sexuelle Orientierung spielen für uns keine Rolle – wir sind offen für die Zusammenarbeit mit jeder Person. 

Beachte bitte: Dies ist eine Stellenanzeige der WongDoody GmbH mit Sitz in Deutschland. Über unsere Website oder andere Quellen kannst Du zu Stellenanzeigen gelangen, die von einer unserer WongDoody-Schwesterfirmen veröffentlicht wurden, mit denen wir weltweit gemeinsam unter der Marke "WongDoody" auftreten. Für jedes Stellenangebot ist ausschließlich das Unternehmen verantwortlich, das die jeweilige Anzeige veröffentlicht hat. Ansprechpartner:innen, erforderliche Angaben und geltende Bedingungen können jeweils abweichen. Hier kannst Du Dich darüber informieren, wie die WongDoody GmbH Deine im Bewerbungsprozess angegebenen personenbezogenen Daten verarbeitet.

Wenn Du Fragen zum Bewerbungsprozess hast, wende Dich gerne direkt an unser People & Culture-Team unter leonie.schaefer@odt.net

A Solution Consultant must be able to build credibility and communicate clearly and concisely. Solutions Consultants perform a wide range of duties; from assisting with RFP responses, conducting detailed requirements analyses, delivering technical presentations and demonstrations to understanding the competitive marketplace. Other responsibilities include working with cross-functional internal organizations to provide relevant customer feedback as input to the product development process. Recommend changes, additions or corrections to product manuals, specification sheets, features, processes, and internal technical procedures when appropriate.

Key Responsibilities:

• Support and enable Five9 technology and reseller partners with presales guidance, training, and ongoing technical coaching
• Effectively communicate plans, progress, and status to both internal and customer organizations
• Partner with Account Executives to sell the Five9 solution into larger and more demanding Enterprise and Strategic companies.
• Articulate the value of Five9’ through presentations, demonstrations, and open discussion with customers.
• Continually seek opportunities to increase customer satisfaction and deepen client relationships by interacting effectively at all levels of the client organization.
• Develop materials in-line with specific sales and/or services opportunity requirements.
• Responsible for transitioning the customer to the services team for implementation.
• Design end-to-end cloud contact centre solutions that integrate omnichannel routing, AI engines, CRM, workforce optimisation, analytics, and enterprise applications.
• Guide customers in responsible AI deployment, including governance, guardrails, and operational oversight using Five9’s AI Trust & Governance framework.
• Support proof-of-concept (POC) and pilot implementations, ensuring measurable customer outcomes.

Key Qualifications:

• Ability to work in a fast-paced environment with minimal supervision.
• Highly organized & able to manage multiple projects & tasks concurrently.
• Exemplary problem-solving ability & enthusiasm.
• Excellent communication & presentation skills with the ability to articulate technology concepts at all customer levels from network engineer to C Level.
• Understanding of enterprise level applications.
• Understanding of IT infrastructure.
• Demonstrated capability working with AI or conversational AI platforms (LLMs, NLP, generative AI, RAG, or virtual agents).
• Ability to think and adapt to a rapidly changing environment and demands.
• Responsible for staying abreast of current technologies and industry trends.
• Ability to deliver demos and presentations that close deals.
• Years of experience 8+ Years Preferred.
• Ability to Travel (40-60%).
• Fluency in English and German; additional European languages beneficial.

Preferred Experience:

• Contact/Call Center Software.
• CRM integration knowledge.
• Integration frameworks.
• Experience with prompt engineering, AI workflow configuration, or low-code integration frameworks.
• IP Telephony (basic networking knowledge assumed).
• PBX, ACD, IVR, Unified Messaging products.
• Workforce engagement, Data Analytics.
• Interaction Analytics, Quality Management, and Interaction recording.

About Atari

Atari is an interactive entertainment company and an iconic gaming industry brand recognized worldwide for its multi-platform games and licensed products. Atari owns and/or manages a portfolio of more than 400 games and franchises, including globally recognized brands such as Asteroids®, Centipede®, Missile Command®, Pong®, and RollerCoaster Tycoon®.

The Atari family of brands includes Digital Eclipse, Nightdive Studios, Infogrames, AtariAge, MobyGames, as well as Coatsink, Early Morning Studios, and Stormteller Games—spanning game development, publishing, and community experiences worldwide.

Atari operates internationally with offices in New York, Paris, Germany, and India.

Overview

Architect, build, and own AI systems that automate expert-intensive technical workflows end-to-end — from CLI frameworks, MCP servers, and agent tooling through to production deployment, business outcome tracking, and continuous improvement. You solve real business problems with AI, ensure solutions are fully implemented and adopted, and measure whether they are actually working.

What You’ll Do

System Architecture and Framework Design

• Own the end-to-end architecture of AI automation systems: workflow decomposition, component communication, human checkpoint placement, and failure behaviour.
• Design and build the internal CLI frameworks, reusable libraries, and agent scaffolding that all pipelines are built on.
• Author and maintain agent instruction files (SKILL.md, CLAUDE.md, system prompts), context configurations, and MCP server definitions that govern agent behaviour and tool access.
• Configure and govern AI coding environments such as Claude Code and Codex CLI: wiring MCP servers, defining tool permissions, building slash commands, and setting the engineering standards the team operates by.
• Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability.

Pipeline Development and Integration

• Build production-grade AI pipelines in Python: orchestration logic, tool definitions, structured prompting, context assembly, output schema validation, and retry and escalation strategies.
• Integrate AI systems with external tooling — version control, build pipelines, platform SDKs, compliance databases, internal APIs — with well-defined permission boundaries and failure handling.
• Design context assembly: how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs.
• Build and operate multi-agent systems: orchestrator-worker patterns, agent memory management, structured handoffs, and conflict resolution between concurrent agents.

Prompt Engineering

• Design, write, and version all system prompts and agent instructions as first-class engineering artefacts: role definition, behavioural constraints, output format contracts, tool usage rules, and fallback handling — with chain-of-thought, few-shot, and structured output techniques applied deliberately per task.
• Own output schema design and prompt regression testing: define format contracts downstream stages depend on, validate against them, and maintain a ground-truth eval set that gates every instruction or model change before deployment.

Context Engineering, Knowledge, and Domain Collaboration

• Engineer context windows with precision: determine what enters each pipeline stage, in what order, and at what granularity — balancing model comprehension and task accuracy against token cost and latency through compression, summarisation, and selective retrieval where needed.
• Work directly with domain experts to extract specialist knowledge and translate it into agent behaviour: decision logic, edge case handling, quality criteria, and failure modes.
• Partner with the RAG Engineer to define retrieval requirements — what knowledge is needed, under what conditions, at what granularity — ensuring retrieved context is precise and actionable.
• Build and maintain structured runtime knowledge assets: curated document corpora, rule sets, decision trees, and validation reference libraries.

Evaluation, Quality, and Reliability

• Build and own the evaluation framework: test suites, regression benchmarks, LLM-as-judge pipelines, per-stage quality metrics, and end-to-end pass rates against expert-validated ground truth.
• Implement production monitoring across all pipelines: latency, token consumption, per-stage success rates, failure distribution, and output quality drift, using tooling such as LangFuse, Arize, or equivalent.
• Run structured failure analysis: diagnose root causes in context assembly, orchestration logic, knowledge coverage, or tool integration — and implement targeted fixes.
• Define and track automation rate as a first-class metric: workflow instances completing end-to-end without human intervention and the verified quality of those outputs.
• Measure and report on business effectiveness of deployed systems: whether the solution is achieving its intended objective, where gaps remain, and what changes would close them.

Governance, Safety, and Technical Leadership

• Implement full audit trails: inputs received, tools called, outputs produced, and human review triggers — making system behaviour traceable and defensible.
• Enforce versioning of all agent instructions, context configurations, and system prompts as engineered artefacts subject to regression testing and controlled rollout.
• Set the technical standard for AI system development within the organisation — architecture patterns, evaluation practices, framework conventions, and quality gates — documented so what you build is maintainable by others.
• Collaborate with engineering, domain, and product teams throughout implementation: answering technical questions, unblocking dependencies, and ensuring the system is correctly adopted by everyone depending on it.
• Engage engineering leadership on roadmap priorities, scope trade-offs, and technical risk with clear technical perspective.

Requirements & Qualifications

• Proven track record of building production AI automation systems from scratch — independently scoping, architecting, and delivering end-to-end from framework design through deployment and ongoing improvement.
• Hands-on expertise with AI coding agents — Claude Code, Codex CLI, Cursor, or equivalent — including MCP server configuration, tool permission design, agent instruction authoring, and CLI-driven workflow automation.
• Experience designing, building, and deploying MCP servers and custom tools from scratch: tool schema design, authentication, permission boundaries, and governing what agents can access and act on.
• Experience creating internal tooling and automation that measurably improved engineering team efficiency — reducing manual processes, accelerating workflows, and enabling teams to operate at higher leverage.
• Experience defining and tracking productivity and efficiency metrics for AI systems: automation rate, time-to-completion reduction, human intervention rate, and business outcome measurement.
• Experience working with data scientists and domain experts to implement AI solutions that measurably improved team productivity and workflow efficiency.
• Deep prompt engineering and context engineering practice: system prompt authoring, few-shot design, chain-of-thought and structured output techniques, token budget management, context window strategy, and prompt versioning with regression testing.
• Proficiency with LLM orchestration frameworks — LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent — with the judgment to know when to use them and when to build leaner.
• Experience building internal CLI frameworks, agent scaffolding, and reusable libraries that others build on.
• Production Python engineering: modular, testable, well-logged code with proper error handling.
• Experience integrating AI systems with external tools and APIs: tool definition design, permission management, rate limit and failure handling.
• Experience building AI evaluation frameworks: test suites, regression benchmarks, LLM-as-judge pipelines, and production quality monitoring.
• Cloud platform experience (AWS, Azure, or GCP): deploying and monitoring AI workloads with containerisation and orchestration tooling.

Preferred / Nice-to-Have

• Experience in the gaming industry: game development pipelines, engine architectures such as Unity or Unreal, platform certification processes, or cross-platform porting workflows.
• Familiarity with game engine scripting, asset pipelines, or platform-specific SDK integration (Xbox GDK, PlayStation SDK, or similar).

To Apply

Please submit your resume and a brief cover letter outlining your experience and interest in the role. If available, you are also welcome to include a link to your portfolio.

EEO Statement

Atari is an equal opportunity employer and we are committed to providing a workplace free from harassment and discrimination.  We are committed to equal employment regardless of race, religion or lack thereof, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, medical condition, veteran status, ancestry, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

AI features in JetBrains IDEs, developed by the IntelliJ AI team, have quickly become a core part of how developers work inside our IDEs. The IntelliJ AI team partners with product groups across JetBrains to embed advanced AI features that accelerate developer workflows and deliver real value to software engineers.

We are currently looking to hire a Senior Machine Learning Engineer to help us realize our ambitious vision of creating AI assistance that supports the entire development lifecycle across JetBrains IDEs. If selected, you will join the ML subteam within IntelliJ AI, driving the development of our ML system from end to end by defining evaluation and metrics, shaping context orchestration, and helping product teams tailor AI capabilities to their needs.

In this role, you will:

• Design and drive evaluation frameworks for AI features, including metrics, experiments, and agent trace analysis.
• Diagnose model performance issues (e.g. prompt drift, context mismatches, and latency/quality trade-offs) and translate findings into actionable improvements.
• Experiment with contexts and lightweight models to continuously develop our ML system.
• Act as the ML liaison for product teams across JetBrains, adapting and scaling AI capabilities in JetBrains IDEs to their needs.
• Build and maintain small helper models (e.g. re-rankers, classifiers, embedding models) to support domain-specific tasks.
• Collaborate with colleagues in ML, product, engineering, and analytic teams to deliver improvements and monitor their impact in production.
• Stay up to date with research in the fields of LLMs, agents, and evaluation, bringing best practices into our workflows.
• Mentor junior engineers and help shape team culture, processes, and tooling around experimentation and evaluation.

We’d be happy to have you on our team if you: 

• Have 5+ years of experience as an ML Engineer, with a solid background in production-grade ML systems (especially LLMs and agent architectures).
• Have experience with LLM evaluation methods and frameworks.
• Can design and run end-to-end experiments – hypotheses, metrics, data collection (including traces/logs), analysis, and decision-making.
• Are skilled in context-aware pipelines or conversational/agent systems.
• Have strong Python programming skills.
• Bring hands-on experience in fine-tuning or training smaller models (e.g. domain-specific fine-tuning and lightweight customizations).
• Communicate clearly and effectively across teams, translating ML/AI insights into product features.
• Have prior mentorship experience with ML/evaluation engineers.
• Thrive in a cross-functional, fast-moving environment, taking ownership, iterating quickly, and delivering results.

We’d be especially thrilled if you have: 

• Familiarity with agent-based systems and orchestrating multi-step reasoning agents.
• Experience with the Kotlin programming language.

#LI-MR1

At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.For over 35 years, the experts at Mitratech have been focused on solving the complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!

Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it's so special to be a part of Mitratech!

Job Overview 

Responsibilities

• Design, deploy, and maintain scalable and secure infrastructure supporting AI and ML workloads.

• Build and maintain AWS cloud environments for compute (EC2, ECS/EKS, Lambda), storage (S3, EFS, FSx), and networking (VPC, Transit Gateway, PrivateLink, Route 53, load balancers).

• Implement security best practices using IAM, KMS, Secrets Manager, GuardDuty, and Security Hub.

• Support and optimize AI/ML workloads across AWS services (SageMaker, Bedrock, Batch, Step Functions).

• Develop and maintain Infrastructure as Code (IaC) using Terraform, AWS CDK, and CloudFormation.

• Manage containerized workloads and orchestration platforms (Docker, EKS, Fargate), including GPU scheduling and scaling.

• Set up and maintain monitoring and observability frameworks using CloudWatch and OpenTelemetry.

• Build and manage CI/CD pipelines (CircleCI, GitHub Actions, GitLab CI) for infrastructure automation and ML/Gen AI deployments.

• Collaborate with ML and Generative AI teams to scale models, optimize performance, and design efficient prompt or inference pipelines.

• Develop runbooks and SOPs for AI service deployment, troubleshooting, and performance optimization.

• Ensure security, compliance, and data protection across AI datasets and environments.

Required Skills & Experience

• Strong proficiency in Linux administration and systems-level troubleshooting.

• Deep expertise in AWS cloud services, with experience in compute, storage, networking, and security domains.

• Proficiency in container orchestration (Kubernetes/EKS) and infrastructure automation tools.

• Hands-on experience with IaC tools such as Terraform, AWS CDK, or CloudFormation.

• Familiarity with monitoring, logging, and observability stacks (Prometheus, Grafana, OpenTelemetry).

• Experience implementing CI/CD pipelines for automated deployment and testing.

• Understanding of AI/ML concepts, including model deployment, inference scaling, and LLM performance tuning.

• Working knowledge of security best practices, IAM roles, encryption, and compliance controls.

• Excellent collaboration and communication skills to partner with ML engineers, data scientists, and product teams.

Education:                         

• A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.

We are an equal-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status.