Machine Learning Engineer

Working hours require overlap until 7:00 PM CET.

Requirements

• 4+ years of experience delivering production AI/ML systems; strong Python, SQL, and software engineering fundamentals, including testing, CI/CD, and code reviews.
• Proven experience building and deploying LLM applications in production, including RAG, embeddings, vector search, and model versioning.
• Experience with model training and evaluation using PyTorch and scikit-learn, including reproducible pipelines and experiment tracking.
• Experience with cloud deployment and operations on AWS and/or Azure, including security/IAM, scalability, and cost awareness.
• Ability to design clean APIs and services, such as FastAPI, and communicate technical trade-offs effectively in English.
• Experience with agentic frameworks such as PydanticAI and LangGraph.

Nice to have

• Experience with information retrieval and search, including hybrid search, reranking, and vector databases.
• Experience with ML platforms and tools such as MLflow, AWS SageMaker/Bedrock, Azure ML, Snowflake, or Databricks.
• Experience with infrastructure as code and containers using tools such as Terraform and Docker.
• Familiarity with responsible AI and AI security practices, including prompt-injection defenses.

Responsibilities

• Deliver production-ready GenAI/ML features for the supply chain risk management domain.
• Collaborate with AI engineers, Data Engineers, Product Managers, and Architects to bring AI solutions into production.
• Build reliable LLM systems, including RAG and multi-agent workflows, with a focus on latency, cost efficiency, and trustworthiness.
• Train and fine-tune models using PyTorch and/or TensorFlow and bring them into production.
• Own model evaluation and rollout, including human-in-the-loop review, A/B testing, and model versioning.
• Operate and monitor AI/ML systems in production, including quality metrics and drift detection.

Benefits

• US and EU projects based on advanced technologies.
• Competitive compensation based on skills and experience.
• Regular performance appraisals to support growth.
• Flexible workspace: remote work or office.
• Bonuses for article writing, public talks, and other activities.
• Generous time off, including vacation, national holidays, sick leave, and family days.
• Personalized learning programs tailored to interests and skill development.
• Free tech webinars and meetups.
• Regular corporate online activities.
• Friendly, supportive team community.