MLOps Engineer

Nasze wymagania: Proven experience running ML/AI systems in production — you’ve dealt with model drift, pipeline failures, and scaling issues in real environments Strong Python skills and hands-on experience with MLOps tooling: MLflow, Kubeflow, Airflow, or similar Solid experience with containerization (Docker) and orchestration (Kubernetes) in production settings Working knowledge of at least one major cloud platform (AWS SageMaker, Azure ML, or GCP Vertex AI) and its ML services Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps) applied to ML workflows Infrastructure as Code experience (Terraform, Pulumi, or CloudFormation) Understanding of ML fundamentals — you don’t need to build models, but you need to understand what makes them break in production Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, or similar) English B2+ — client-facing role, calls and written communication included Mile widziane: Experience with LLM serving infrastructure (vLLM, TGI, Triton Inference Server) Databricks, Azure AI Foundry, or AWS Bedrock GPU infrastructure management and cost optimization Kafka or streaming pipelines for real-time inference Experience with vector databases (Qdrant, Weaviate, Pinecone, pgvector) in production RAG setups Familiarity with AI governance and regulatory context (EU AI Act, GDPR) O projekcie: We are looking for an MLOps Engineer who knows that a model is only as good as the pipeline behind it — someone who has actually kept ML systems running in production, not just deployed a tutorial to a notebook. You will work on international projects for clients in banking, insurance, and telco (US, Netherlands, UK), building the infrastructure that makes AI reliable at scale. Zakres obowiązków: Designing, building, and maintaining CI/CD pipelines for ML model training, evaluation, and deployment Managing model lifecycle end-to-end — from experiment tracking and versioning to production serving and monitoring Setting up and maintaining infrastructure for ML workloads on cloud platforms (AWS, Azure, or GCP) Implementing monitoring, alerting, and observability for deployed models — detecting drift, latency issues, and quality degradation Building and managing feature stores, data pipelines, and ETL processes that feed ML models Containerizing and orchestrating ML services using Docker and Kubernetes Collaborating with data scientists and ML engineers to streamline the path from experimentation to production Implementing Infrastructure as Code (Terraform, Pulumi, or CloudFormation) for reproducible ML environments Defining and enforcing MLOps best practices, standards, and documentation across teams Oferujemy: Certifications and training funded Private medical care (Medicover) Multisport card English language classes Flexible working hours Team meetups and integration events Referral bonus