Company Background

Our client is a global technology company transforming the way brands and agencies make marketing decisions. Its platform enables marketers to plan and activate cross-channel, programmatic media campaigns using real-time research, proprietary audience data, advanced analytics, and more than 150 integrated partners including Facebook, Instagram, Pinterest, Snapchat, and Twitter. The company operates across North America, Europe, the Middle East, Asia, and Australia.

Project Description

As a member of the DevOps team, you will be responsible for delivering solutions to your assigned team and scalable, reusable solutions across the entire engineering organization. You will operate infrastructure and ensure services are monitored, reliable, automated, and scalable, supporting tasks from server provisioning and application deployments to troubleshooting. You will also help bridge the gap between development and operations, collaborating across engineering and business teams.

Technologies

• AWS
• Kubernetes
• Linux admin
• OpenTelemetry/Grafana/Prometheus/Elasticsearch
• CI/CD
• ArgoCD
• Bash/Python
• Ansible/Chef
• GitLab
• Airflow
• Spark

What You'll Do

• Contribute to the implementation and design of systems and networks used to operate production infrastructure with a focus on automation;
• Collaborate with developers on operations-related issues, providing support and guidance;
• Develop tools to monitor infrastructure and custom applications;
• Troubleshoot issues across the entire stack;
• Document system design and application operation procedures;
• Build, monitor and tune production systems;

Job Requirements

• Experience with or knowledge of AWS and physical infrastructure (other cloud is a plus);
• Experience with or knowledge of automation tools (Terraform or Ansible);
• Troubleshooting skills and experience with Linux;
• Understanding of Linux, networking, and internet fundamentals;
• Understanding of monitoring infrastructure and applications (e.g. Grafana, Prometheus, InfluxDB);
• Scripting skills in one or more of the following: Bash, Python;
• Experience with containers and orchestration tools such as Docker and Kubernetes (or similar);
• Familiarity with CI/CD tools (e.g. GitLab CI, GitHub Actions, Jenkins);
• Confident use of Git, including branching, pull requests, and code review;
• Basic understanding of security concepts, including secrets management, IAM, and HTTPS/TLS;
• Ability to work with logs and identify the root cause of failures;
• Ability to work independently and deliver tasks with minimal supervision;
• Readiness to participate in on-call / shift duties on a rotating schedule;
• English: B1+ level;

Nice To Have

• Experience with highly loaded production systems;
• Experience with support services such as Consul, Zookeeper & ELK;
• Experience with noSQL solutions (Couchbase a plus);
• Experience with big data systems (EMR, Spark & Hive a plus);

What Do We Offer

The global benefits package includes:

• Technical and non-technical training for professional and personal growth;
• Internal conferences and meetups to learn from industry experts;
• Support and mentorship from an experienced employee to help you professional grow and development;
• Health insurance;
• English courses;
• Sports activities to promote a healthy lifestyle;
• Flexible work options, including remote and hybrid opportunities;
• Referral program for bringing in new talent;
• Work anniversary program and additional vacation days.

Company Background

Our client is a global technology company transforming the way brands and agencies make marketing decisions. Its platform enables marketers to plan and activate cross-channel, programmatic media campaigns using real-time research, proprietary audience data, advanced analytics, and more than 150 integrated partners including Facebook, Instagram, Pinterest, Snapchat, and Twitter. The company operates across North America, Europe, the Middle East, Asia, and Australia.

Project Description

Data Management Platform is the core system that receives, processes and provides information to other external DMPs and real time platforms. The purpose of DMP is to store the connection between users/devices and their respective behavioral categories. Further this data is used by the other teams for the analytics and digital ad targeting. Team roadmap includes developing a new system based on the streaming architecture with a relevant tech stack.

Technologies

• Spark
• Scala
• Vertica
• Kafka
• Airflow
• K8S
• Python, Java (preferable)

What You'll Do

• Own end-to-end development, from design to production, with a focus on innovation and efficiency;
• Lead and mentor developers on complex projects impacting core business systems;
• Contribute to the design and development of next-generation data solutions;
• Implement high-scale big data solutions and contribute to platform infrastructure and architecture;
• Research and evaluate core technologies, integrations, and external APIs;
• Collaborate with cross-functional stakeholders, including Product, Engineering, and data providers;
• Participate in an off-hours pager duty rotation;

Job Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent experience;
• 4+ years of software development experience;
• 2+ years of experience with Apache Spark;
• 2+ years of experience with Scala (preferred) and/or Python or Java;
• Experience working with high-performance, large-scale, or distributed data systems;
• Experience with SQL and NoSQL databases;
• Experience with Kubernetes;
• Experience with Git;
• Experience building and optimizing data pipelines, ETL processes, and streaming systems (e.g., Hadoop, Spark, Kafka) is a plus;
• Familiarity with tools such as GitLab, Grafana, InfluxDB, and Kibana is a plus;
• Strong communication skills and ability to work effectively in a team;
• English level: B1+ or higher;

What Do We Offer

The global benefits package includes:

• Technical and non-technical training for professional and personal growth;
• Internal conferences and meetups to learn from industry experts;
• Support and mentorship from an experienced employee to help you professional grow and development;
• Health insurance;
• English courses;
• Sports activities to promote a healthy lifestyle;
• Flexible work options, including remote and hybrid opportunities;
• Referral program for bringing in new talent;
• Work anniversary program and additional vacation days.

Why Kimchi?

Kimchi is the AI platform inside CAST AI. We started by helping companies run LLMs on their own Kubernetes clusters - now we're building the execution layer where agents do real work.

Our Infrastructure today: multi-model inference (MiniMax, Kimi, GLM-5, Nemotron, DeepSeek) with intelligent routing, an OpenAI-compatible API, and deployment flexibility from our GPUs to your VPC. The inference layer is the foundation. What we're hiring for sits on top of it: coding agents, agent runtimes, orchestration systems, and the reliability engineering that makes them actually finish things.

Tech Stack: TypeScript, Go, Kubernetes, AWS/GCP/Azure, MCP, Prometheus/Grafana/Loki, GitLab CI, ArgoCD.

Why harness engineering matters here
OpenAI and Anthropic ship models. They also ship one harness each - the scaffolding that turns a raw model into something that can plan, execute, recover, and complete work. We ship a different kind of harness: one built for cost-conscious, long-horizon autonomy, running on inference infrastructure we control end-to-end.
A decent model with a great harness beats a great model with a bad harness. We've watched this play out. The gap between what today's models can do and what you see them doing is largely a harness gap - and that gap is where we operate.

What you'll build
The ratchet.
Every time our agent makes a mistake, we engineer a solution so it never makes that mistake again. That means hooks that enforce constraints the model "knows" but forgets: pre-commit lint checks, permission gates, context compaction before the window fills. Success is silent, failures are verbose.

Long-horizon execution.
Our harness is built around spec-driven autonomy: meta-prompting, fresh context per task, worktree-per-slice git strategy, automatic replanning, crash recovery, stuck detection. We're implementing Ralph loops - when the model tries to exit, we intercept and reinject the goal into a fresh context. The agent reads state from disk and continues. Multi-session, multi-day work, without context rot.

Planner/executor splits.
Planning with a reasoning model, executing with a fast one, evaluating with a third. Separating generation from evaluation beats self-verification because agents reliably skew positive when grading their own work.

The harness surface.
CLI, TUI, MCP integration, sandboxed execution, telemetry. Our AGENTS.md is short - every line traces to a specific thing that went wrong. TypeScript on the surface, Go where it matters.

Memory and context.
Moving agents off laptops, giving them state that survives across sessions, managing context so information lands where it's actionable. Compaction, tool-call offloading, progressive skill disclosure.

What makes this different (with receipts)
You've seen the pitch: "we route to the best model." Everyone says that. Here's what we actually have:

• GPU infrastructure we own. Not just an API reseller. From GPU placement across clouds to the inference endpoint your agent calls - we control the cost curve.
• A harness-first thesis. We treat agent failures as configuration problems, not model problems. When we moved from a stock harness to our own, completion rates on internal benchmarks improved by 40%+ on the same model.
• Agents.md that earns every line. No brainstormed rules - every constraint in our system prompt traces to a real failure we saw and fixed.

Requirements:

• You've used AI coding agents in anger. Not demos - real work. You have opinions about Claude Code, Codex, OpenCode, Cursor. You know what it feels like when an agent gets stuck and why.
• Strong TypeScript or Go in production. Comfort moving between them. Our surface is TypeScript; our core is Go.
• You think in harness terms. You read "the agent hallucinated" and your first instinct is to ask what context it was missing, what hook should have caught it, what constraint should exist.
• You drive features end-to-end. Design → build → ship → measure → iterate. We don't have layers that absorb ambiguity for you.

Responsibilities:

• Build and evolve the agent harness - ship hooks, permission gates, and context compaction. Every AGENTS.md constraint traces to a failure you personally diagnosed.
• Own long-horizon execution - multi-session task completion via spec-driven prompting, worktree-per-slice git, Ralph loop recovery, and stuck detection. Completion rate is your metric.
• Architect planner/executor/evaluator pipelines - planning with a reasoning model, execution with a fast one, evaluation with a third. No self-verification.
• Manage agent memory and context - state persistence across sessions, context compaction, tool-call offloading. Zero context rot on multi-day work.
• Own the harness surface - CLI, TUI, MCP integrations, sandboxed execution, telemetry. TypeScript on the surface, Go where it matters.

What success looks like (after 6 months):

• You've shipped at least one major harness feature end-to-end: designed it, built it, measured it, iterated.
• You've added constraints to our AGENTS.md based on failures you personally observed and diagnosed.
• You've improved a measurable reliability metric - completion rate, context efficiency, or cost per successful task.
• You've formed strong opinions about where our harness is load-bearing and where it's dead weight.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience).
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. 

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

We are hiring across multiple teams!

As a Senior Software Engineer, you will have the opportunity to work on different key features of our product. We are currently hiring Senior Software Engineers for the following teams:

- Workload Optimization - Automates workload resource management by dynamically adjusting resource allocations, helping developers significantly reduce costs and improve application reliability.

- Karpenter - The Karpenter team powers the integration between Karpenter and Cast AI, bringing enterprise capabilities to the most popular open source Kubernetes autoscaler. We enhance Karpenter with advanced features that improve application reliability and performance while optimizing costs. By joining the team, you’ll bridge open source innovation with enterprise requirements, directly impacting how organizations run Karpenter at scale.

- Reporting - Builds a scalable reporting system that ingests millions of rows per second into our time-series databases, providing insights into cost savings, workload efficiencies, and Cast AI automation impact.

- Pricing - Drives the synchronization of public and customer cloud resources, availability, and dynamic pricing across all major cloud providers. Empowers autoscaling by leveraging discounts, commitments, and cross-cluster tracking to maximize savings. Provides a reliable source of truth for node pricing, resources, components, discounts, and commitments.

- Autoscaler - Automates Kubernetes node autoscaling to optimize clusters, balance workloads, remove underutilized nodes, and dynamically allocate capacity in real-time, thereby reducing cluster costs by half.

- Identity -  Builds and maintains the trust and access foundation for the entire platform, ensuring every user, service, and workload authenticates and interacts securely and seamlessly at scale.

- Billy -  Powers the critical day-2 operations layer of the platform - from billing and audit trails to notifications and feature flags - ensuring the platform runs reliably, transparently, and at scale for every customer, every day.

Here are some of the tools we use daily:

• Programming Languages: Go
• Cloud & Orchestration: Kubernetes, AWS, GCP, Azure
• Databases & Storage: PostgreSQL, Cloud Object Storage
• Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs
• Observability: Prometheus, Grafana, Loki, Tempo
• CI/CD & GitOps: GitLab CI with ArgoCD.

Requirements:

• Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.
• Deep hands-on experience with cloud platforms (AWS, GCP, or Azure) - including real understanding of how compute, networking, and storage work under the hood.
• Understanding of Kubernetes internals - autoscaling and networking.
• You've personally driven a complex project end-to-end.
• Strong debugging, optimization, and performance-tuning skills - including query profiling, index design, and database performance tuning beyond ORM usage.
• You've run observability tooling (Prometheus, Grafana, OpenTelemetry) in production.
• CI/CD and DevOps practices experience.
• Strong English skills, both verbal and written.
• Startup mindset: adaptable, proactive, and comfortable with ambiguity.

Responsibilities:

• Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale.
• Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management.
• Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1-4 weeks).
• Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services.
• Collaborate with product and other engineering teams to solve problems that don't have textbook solutions.
• Work with time-series data, cloud provider APIs, and Kubernetes control plane internals.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• Technical interview (system design)
• Live coding
• Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

About Sisense

Sisense is a leading AI-powered Analytics Platform as a Service (AnPaaS) that empowers product teams and developers to embed conversational, predictive, and agentic intelligence directly into applications and workflows. Our API-first, developer-first platform turns complex data into faster, smarter, actionable decisions for over 2,000 global customers across financial services, retail, healthcare, and technology.

With the recent launch of Sisense Intelligence (Intelligence Assistant, MCP server, Managed LLM), we are accelerating innovation in embedded analytics at scale.

The Role

As a DevOps Engineer, you will play a key role in designing, building, and maintaining the scalable, reliable infrastructure that powers Sisense’s AI-powered embedded analytics platform. You will work on cloud-native environments, CI/CD pipelines, automation, monitoring, and production stability — helping ensure our platform delivers high performance and developer experience at scale.

This is a hands-on role with significant ownership and impact on both internal development velocity and customer-facing reliability.

Key Responsibilities

• Design, implement, and maintain scalable infrastructure for our SaaS platform using modern cloud-native technologies.
• Build and improve CI/CD pipelines, automation tools, and deployment processes to increase development velocity and reliability.
• Own production environments: monitoring, troubleshooting, performance tuning, and incident response.
• Work closely with development teams to support infrastructure needs for new features and services.
• Research, evaluate, and introduce new tools and technologies to improve platform efficiency and observability.
• Develop and maintain Infrastructure as Code (Terraform, Crossplane, Helm, Argocd, etc.).
• Collaborate on improving observability, logging, and alerting across distributed systems.
• Participate in a rotating on-call schedule to provide 24/7 coverage for incident response and production support.

Requirements

• 4–6+ years of hands-on DevOps or SRE experience.
• Strong proficiency with Kubernetes and cloud platforms (AWS, Azure, or GCP) AWS preferred.
• Solid experience with Infrastructure as Code tools (Terraform /crossplane / Terragrunt, Helm, argocd).
• Excellent Linux systems knowledge and scripting skills (Bash, Python, or Go preferred).
• Experience building and maintaining CI/CD pipelines and automation frameworks (gitlab-ci ,github-actions and jenkins).
• Deep familiarity with monitoring tools (fluentd , fluent-bit, promethus and grafana stack , opentelemetry )
• Strong problem-solving and troubleshooting skills in high-scale production environments.
• Good communication skills and ability to work effectively in a remote, distributed team.
• Advanced English.

Nice to Have

• Experience with Node.js / TypeScript environments.
• Familiarity with multiple linux distros (ubuntu-debian , redhat , oracle)
• Background in data-intensive or analytics platforms.
• Familiarity with OpenTelemetry, monitoring tools, or distributed tracing.
• Experience supporting customer-facing SaaS platforms at scale.

Why Join Sisense

This is a high-impact role where you’ll work on the infrastructure behind cutting-edge embedded AI analytics. You’ll join a talented global engineering team, enjoy competitive compensation, meaningful equity, and the opportunity to solve complex technical challenges in a fast-growing company.

If you’re a strong DevOps Engineer who loves building reliable, scalable systems and wants to work on AI-powered data solutions, we’d love to hear from you.