Let's start with the role

As a Principal Data Scientist, you will play a critical role in leveraging data and AI to make decisions in real time to deliver the right reward and message to the right customer at the right time and medium. and create a competitive advantage for the organization The ideal candidate will be a tenured Data Scientist, ML or AI engineer that has a proven track record of designing and implementing cutting-edge, real-time and AI-empowered applications along with the corresponding ML/DL algorithms.
As a Principal Data Scientist you will:
Architecture: Design scalable, reliable and high-performance AI applications along with their corresponding ML/DS algorithms.
Innovation: Set the tech direction in the AI tribe in order to deliver state-of-the-art AI models in production.
Mentorship: Inspire and mentor fellow data scientists, fostering technical and soft skill growth.
Pioneering: Stay ahead of the AI curve, applying emerging technologies to enhance our capabilities, disrupt the industry and showcase what’s truly possible.
Strategic Planning & Prioritization: Define the company-wide AI technical and product roadmap, in-line with the business product roadmap. Resist the AI hype and enable impact-driven prioritization with quick and impactful PoCs that demonstrate the value.
Autonomy & end-to-end Ownership: From ideation, to translation of business needs to technical requirements, all the way to operationalization and maintenance, demonstrate autonomy on high-complexity and ambiguous projects and ownership of the outcome.

What you’ll bring

Must have:
8+ years of hands-on experience leveraging large-scale data to build ML/DS models with Python
PhD-level expertise in one or more of the following domains: Recommendation Systems,,Time-series, Reinforcement Learning, Graph Learning
A deep understanding of the ML development lifecycle and how to optimize the trade-offs in each step. 
Proven track record of designing, implementing, optimizing and scaling performant AI-empowered applications. Solid experience with MLOps and quality evaluation practices in order to operationalize and maintain those applications in production.
Excellent communication and collaboration skills targeting a diverse audience from stakeholders to cross-functional technical teams, in order to provide technical guidance and insight.

Nice to have:
Expertise in constraint optimization, uplift modeling. causal inference & personalization 
Exposure to a variety of domains especially CRM and a diverse family of models. 
Familiarity with a wide range of big data technologies, such as Apache Spark, Delta Lake, Apache Kafka, Apache Flink and NoSQL databases.

We're looking for an AI expert who wants to be at the forefront of innovation in the iGaming industry. Join us in shaping the future of iGaming with breathtaking experiences that thrill and delight players around the world.

Kaizen Gaming Perks

• 🕑 Hybrid way of working & remote options
• 🏃 A buddy will support you with your onboarding
• 💸 Competitive pay and bonus scheme
• 👩‍⚕️ Private health insurance for you and your family
• 💰 Monthly meal allowance 
• ⭐Developmental 360° feedback framework
• 📚 Unlimited access to Udemy & continuous training
• #li-ap1

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.

About the role

You'll join one of several teams building the low-level systems behind CAST AI's multi-cloud automation platform. The work sits at the intersection of Kubernetes, cloud infrastructure, and Linux.

Depending on your strengths, you'll work in one of these areas:

• Cluster and node lifecycle - custom node bootstrapping across AWS, GCP, and Azure. Controllers and operators managing full node lifecycle. Bridging the gap between autoscaler decisions and running VMs - including working around limitations of managed Kubernetes platforms like EKS, GKE, and AKS.
• Cross-cloud provisioning - orchestrating infrastructure across regions and clouds. Workflow orchestration, provisioning integrations, networking and identity across cloud boundaries.
• Live container migration - zero-downtime relocation of running containers using CRIU checkpoint/restore, custom CRI-O and runc forks, and forked cloud CNI plugins. Memory page transfer, TCP session migration, process-level checkpoint/restore. Kernel-level Linux engineering applied to Kubernetes.
• Storage optimization - dynamic volume rightsizing, BtrFS compression, custom CSI drivers. Full storage stack from cloud disk APIs through filesystem internals to Kubernetes PV/PVC lifecycle.

Common across all teams: Go, deep Kubernetes, multi-cloud, building beyond what the ecosystem offers.

How we build

We invest heavily in agentic development and AI-powered tooling. Engineers work with code agents and automated workflows daily. We expect you to shape how these tools evolve.

Requirements:

• Strong backend fundamentals and production-grade coding, preferably Go.
• Hands-on Kubernetes internals, cloud infrastructure, or large-scale distributed systems experience.
• Clear English communication, collaborative and self-directed.
• Bias toward action and ownership.

What would make you stand out

• Deep knowledge of EKS, GKE, or AKS internals.
• Low-level Linux experience: process internals, networking (NAT, iptables, conntrack, SDN), filesystems, storage.
• Kubernetes or cloud-native OSS contributions.
• Cloud storage depth - block storage, CSI, volume management, filesystem internals.
• Container runtime experience (CRI-O, runc, containerd) or CRIU.
• Experience with AI coding agents and agentic development workflows.
• Comfort building where there's no playbook.

Responsibilities:

• Design, build, and operate backend services, Kubernetes operators, and controllers in Go across AWS, GCP, and Azure.
• Solve infrastructure-layer problems — networking, scheduling, storage, compute, or container runtime internals.
• Build and maintain high-throughput services (gRPC/REST) and async orchestration workflows.
• Shape public and internal APIs.
• Leverage and improve AI-powered development workflows.
• Participate in design reviews, code reviews, on-call, and customer deep-dives.

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

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

Let's start with the role

As the team lead of an AI product team you will lead and guide the team's efforts in delivering high impact AI products. You will be at the core of AI developments, analyzing data, building machine learning models, collaborating with the tech team to create successful AI products. 

The ideal candidate will combine a deep understanding of machine learning algorithms and proven extensive experience in building  ML/AI powered applications in production along with previous experience of leading and managing AI teams.

As a Data Science Team Lead, you will:

• Manage a team of 5 to 7 skilled data scientists and machine learning engineers. 
• Translate product requirements to machine learning problems and identifying areas where AI can have the most business impact;
• Analyze data to identify significant patterns and generate insights;
• Perform exploratory data analysis (EDA) and feature engineering to support the modeling process;
• Implement best practices on model selection, parameter tuning and optimization.
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production environments;
• Mentor and share knowledge with more junior members and guide the team through complex projects. 

What you’ll bring

• Previous experience as a people manager;
• Expert knowledge of machine learning algorithm and their theoretical foundations;
• Extensive hands-on experience in delivering machine learning models to production;
• Experience in developing MLOps solutions;
• Knowledge of Deep learning;
• Proficiency in Python’s machine learning ecosystem and solid software engineering skills (OOP);
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

• Experience with Big Data tools  (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Previous experience in pricing optimisation;
• Previous experience in forecasting;
• Previous experience in the betting industry.

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-HYBRID
• #LI-AP

Let's start with the role

At Kaizen, we strive to harness the power of modern AI algorithms to continuously improve our product and business. Kaizen’s AI department consists of product, platform and research teams and lies at the core of that mission. We develop AI products for various applications, ranging from detection of fraudulent activity to personalized gaming recommendations and from AI-powered co-pilots to CRM optimizations. 

We structure our teams to be self-driven, autonomous, and cross-functional. Within these teams, you will have the opportunity to work with Data Scientists, Machine Learning Engineers, Data Engineers, Data Analysts, Product Owners and Agile Delivery Leads.

As a Senior Machine Learning Software Engineer, you will focus on delivering robust and scalable Machine Learning pipelines and you will support the operationalization of Machine Learning models. 

• Collaborate closely with data scientists in order to deliver production-ready model training pipelines;
• Design scalable solutions in a distributed environment;
• Design scalable inference pipelines and architectures;
• Implement model diagnostics such as evaluation, monitoring flows etc.;
• Automate model deployment, enable REST API model endpoints;
• Develop scalable model serving flows;
• Work on cluster optimization and scalability;
• Develop and maintain internal tools and libraries;
• Write CI/CD/CT pipelines.

What you’ll bring

• Extensive experience in software development using Python in large scale, high-performance production environment;
• Significant hands-on experience in designing ML software systems;
• Significant experience with cloud environments like Microsoft Azure or AWS;
• Solid understanding of:
• The ML project lifecycle;
• MLOps concepts and tools such as MLflow or Kubeflow;
• ML algorithms and models.

• Hands-on experience with Spark and PySpark;
• Excellent problem solving and communication skills.

#Machine Learning (ML) #MLOps #AIOps #Software Engineering

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-EL1

Let's start with the role

As a Senior Data Scientist, you will be at the forefront of our AI developments,  analyzing data, building machine learning models to support our AI products and working along with the rest of the tech team to create successful AI products. The ideal candidate will have a deep understanding of machine learning algorithms and proven extensive experience in building  ML/AI powered applications in production.

As a Senior Data Scientist you will:

• Translate product requirements to machine learning problems and  identifying areas where AI can have the most business impact;
• Analyze data to identify significant patterns and generate actionable insights;
• Perform exploratory data analysis EDA and feature engineering to support the modeling process;
• Apply best practices on model selection, parameter tuning etc.;
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production environments;
• Mentor and guide junior members, sharing knowledge and leading complex projects. 

What you’ll bring:

Must have:

• Expert knowledge of machine learning algorithm and respective theory;
• Extensive hands-on experience in delivering machine learning models to production;
• Experience in developing MLOps solutions;
• Knowledge of the Python machine learning ecosystem and solid software background in OOP;
• Knowledge of Deep learning;
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

Nice to have:

• Experience with Big Data tools  (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Experience in recommendation systems;
• Experience in GenAI application development;
• Relevant industry experience i.e. Fraud detection, CRM, Sportsbook etc.

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-gs1

Let's start with the role

As a Senior Data Scientist, you will be at the forefront of our AI developments,  analyzing data, building machine learning models to support our AI products and working along with the rest of the tech team to create successful AI products. The ideal candidate will have a deep understanding of machine learning algorithms and proven extensive experience in building  ML/AI powered applications in production.

As a Senior Data Scientist you will:

• Translate product requirements to machine learning problems and  identifying areas where AI can have the most business impact;
• Analyze data to identify significant patterns and generate actionable insights;
• Perform exploratory data analysis EDA and feature engineering to support the modeling process;
• Apply best practices on model selection, parameter tuning etc.;
• Run comparative experiments for model training;
• Analyze ML metrics to evaluate different potential solutions;
• Manage the full lifecycle of AI features, from data collection to model design and to implementation and optimization in production environments;
• Mentor and guide junior members, sharing knowledge and leading complex projects. 

What you’ll bring:

Must have:

• Expert knowledge of machine learning algorithm and respective theory;
• Extensive hands-on experience in delivering machine learning models to production;
• Experience in developing MLOps solutions;
• Knowledge of the Python machine learning ecosystem and solid software background in OOP;
• Knowledge of Deep learning;
• Strong skills in teamwork, communication, and analytical thinking;
• Fluency in English, both oral and written.

Nice to have:

• Experience with Big Data tools  (Spark/PySpark) and cloud environment, ideally Azure / Databricks;
• Experience in recommendation systems;
• Experience in GenAI application development;
• Relevant industry experience i.e. Fraud detection, CRM, Sportsbook etc.

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-gs1

Let's start with the role

At Kaizen, we strive to harness the power of modern AI algorithms to continuously improve our product and business. Kaizen’s AI department consists of product, platform and research teams and lies at the core of that mission. We develop AI products for various applications, ranging from detection of fraudulent activity to personalized gaming recommendations and from AI-powered co-pilots to CRM optimizations. 

We structure our teams to be self-driven, autonomous, and cross-functional. Within these teams, you will have the opportunity to work with Data Scientists, Machine Learning Engineers, Data Engineers, Data Analysts, Product Owners and Agile Delivery Leads.

As a Senior Machine Learning Software Engineer, you will focus on delivering robust and scalable Machine Learning pipelines and you will support the operationalization of Machine Learning models. 

• Collaborate closely with data scientists in order to deliver production-ready model training pipelines;
• Design scalable solutions in a distributed environment;
• Design scalable inference pipelines and architectures;
• Implement model diagnostics such as evaluation, monitoring flows etc.;
• Automate model deployment, enable REST API model endpoints;
• Develop scalable model serving flows;
• Work on cluster optimization and scalability;
• Develop and maintain internal tools and libraries;
• Write CI/CD/CT pipelines.

What you’ll bring

• Extensive experience in software development using Python in large scale, high-performance production environment;
• Significant hands-on experience in designing ML software systems;
• Significant experience with cloud environments like Microsoft Azure or AWS;
• Solid understanding of:
• The ML project lifecycle;
• MLOps concepts and tools such as MLflow or Kubeflow;
• ML algorithms and models.

• Hands-on experience with Spark and PySpark;
• Excellent problem solving and communication skills.

#Machine Learning (ML) #MLOps #AIOps #Software Engineering

Kaizen Gaming Perks

• 🕑 Hybrid way of working;
• 🏃 A buddy will support you with your onboarding;
• 💸 Competitive pay & bonus scheme;
• ⭐Developmental 360° feedback framework;
• 💰 Monthly meal allowance;
• 👩‍⚕️ Private health insurance for you and your family;
• 📚 Unlimited access to Udemy & continuous training;
• 👨‍👩‍👧‍👦 Family Support.
• #LI-Hybrid
• #LI-EL1

What Is The Role : 

Developers and community matter. Our users and contributors have helped ensure that the Elastic Stack (including Elasticsearch, Kibana, Beats, and Logstash) is more than just code — they are open source projects that people love to use.

We’re looking for someone who can connect with our global audience through creative, educational, and visually engaging content. You don’t need to be a Senior Engineer — but you should have a strong technical foundation, curiosity to learn, and the skills to tell compelling stories about technology.

Your work will combine writing, visuals, and video — with no waiting on others to get it done. From drafting a Twitter thread in the morning to publishing an infographic and editing a short video in the afternoon, you’ll take ideas from concept to launch, end to end.

What You Will Be Doing:

• Create engaging technical content for social media, blogs, and YouTube, covering Search, AI, and Elasticsearch in ways that are approachable and inspiring. Topics range from foundational and evergreen topics to today's LLM release.
• Write short-form content (Twitter threads, LinkedIn posts) that distills complex concepts into clear, shareable insights.
• Produce simple, high-quality videos and infographics yourself — from scripting and recording to editing and publishing.
• Explore new trends in Search, AI, and developer tools, and translate them into content our audience cares about.
• Build demos and examples to support your content or share the highlights of the latest research papers.
• Add a creative spark to Elastic’s content to keep our community engaged and draw in new audiences.

What You Bring : 

• Solid technical background and experience in an engineering role.
• Demonstrated ability to create a variety of content formats: blog posts, social media threads, videos, infographics.
• Comfortable working independently and seeing projects through from idea to publication without waiting on others.
• Curiosity and adaptability to quickly pick up new tools and topics.
• A collaborative mindset and willingness to share ideas, in a fully distributed team.
• Strong written and spoken communication skills in English.

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.

About the role

Databases are notoriously hard to optimize—with 350+ configuration parameters in PostgreSQL alone, even experienced engineers struggle to find the right settings. Have you ever dreamed about a database that continuously self-optimizes itself in the background, so you can focus on what really matters: building great products?

Sometimes your queries slow down and it's not immediately clear why. Imagine a system that proactively detects performance degradation, analyzes the root cause, and automatically improves your queries and indexes for you—creating pull requests with the exact optimizations needed.

CAST AI is seeking a skilled senior software engineer with expertise in databases and interest in understanding the underlying technology that makes them work. Knowledge of implementation details around transactions, wire protocols, WAL, indexes, query planning, and other fundamental database features will enable you to build a product that automates what's traditionally been manual and complex.

You'll work on a greenfield project with high impact, creating intelligent systems that make database optimization effortless for our customers.

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.

Requirements:

• Experience working with and troubleshooting PostgreSQL and/or MySQL in production environments
• Experience troubleshooting database-related issues in one of your previous roles—whether as a DBA, backend engineer, SRE, or any position where you've debugged slow queries, optimized indexes, or resolved production database problems
• Strong programming skills in Go and/or C++
• Strong problem-solving skills and the ability to troubleshoot complex issues in a production environment
• Strong written and verbal communication skills in English
• Ability to work independently and collaboratively within a team
• Startup mindset: adaptable, proactive, and comfortable with ambiguity
• A proactive, problem-solving mindset with a "yes we can" attitude

Here are some of the tools we use daily:

• Mostly Golang with C++ in the most critical data pathways
• Extending Envoy with custom network filters
• ClickHouse and PostgreSQL for business metrics
• GRPC and REST APIs
• GitLab CI with ArgoCD as our GitOps CD engine
• Prometheus, Grafana, Loki, and Tempo for observability
• Deployment charts are written in Helm.

Responsibilities:

• Design, build, and operate database optimization services in Go and C++.
• Develop features for query analysis, index recommendations, and automated performance tuning that deliver measurable database cost savings.
• Extend and maintain integrations with PostgreSQL and MySQL protocols, cloud database services (RDS, Cloud SQL, Azure Database).
• Implement query parsing, plan analysis, and recommendation algorithms that identify optimization opportunities.
• Develop monitoring and alerting systems for database performance metrics, connection health, and cost tracking.
• Shape public and internal APIs with an eye for simplicity and developer experience.
• Participate in design reviews, code reviews, and occasional customer deep‑dives.

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.
• Private health insurance.
• 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.

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