Senior AI Software Engineer

Join Bayer's Machine Learning and Artificial Intelligence unit within the Enterprise Data & Analytics Platform. The team works internationally across Poland, Germany, Spain, and India with LLMs, embeddings, classic ML, and optimization on a modern cloud-native stack including Python, AWS, Azure, and Databricks.

• Master’s degree (or equivalent) in Computer Science, Data/AI, Mathematics, or related field; PhD is an advantage.
• 5+ years professional experience in AI/software/ML engineering with end-to-end product delivery.
• Advanced Python and production-grade API development (REST/gRPC), authN/authZ (OAuth2/OIDC), rate limiting.
• Expertise in containerization (Docker); Kubernetes experience is a plus.
• Proficiency in AWS (Lambda, ECS/Fargate/EKS, API Gateway, S3, RDS, Secrets Manager, Bedrock) and/or Azure (Functions, AKS, API Management, Storage, PostgreSQL/Cosmos DB, Key Vault, Azure OpenAI).
• Strong CI/CD knowledge, especially GitHub Actions.
• Infrastructure as Code experience (preferably Terraform).
• Solid understanding of LLMs and embeddings: context management, tool calling, streaming, latency/cost trade-offs.
• Monitoring and traceability mindset: OpenTelemetry, Langfuse/LangSmith.
• Strong software engineering fundamentals: testing, code reviews, error handling, reliability/resilience.
• Excellent problem-solving and communication skills; fluent in English.
• Preferred: Hands-on with agent frameworks (LangChain, LangGraph, PydanticAI), FastAPI, FastMCP for MCP server development.
• Preferred: RAG and vector search proficiency (pgvector, OpenSearch).
• Preferred: Experience with relational databases (PostgreSQL); Databricks experience a plus.

• Industrialize and scale GenAI prototypes into secure, resilient IT products for Enabling Functions.
• Design, implement, and operate cloud-native APIs and microservices for AI workloads using Python and FastAPI, following schema-first design (OpenAPI/gRPC).
• Develop Model Context Protocol (MCP) servers (FastMCP) ensuring robust permissions and auditing.
• Architect agent workflows with LangChain, LangGraph, and PydanticAI (tool calling, memory, event-driven orchestration).
• Build reliable text-to-SQL solutions and RAG services with high-quality embeddings, indexing, reranking, and caching.
• Implement CI/CD pipelines with automated testing using GitHub Actions.
• Deploy on AWS and/or Azure using containers, serverless, API gateways, managed databases, and object storage.
• Ensure end-to-end observability with structured logging, OpenTelemetry tracing, and model/agent monitoring (Langfuse/LangSmith/MLflow).
• Establish safety and quality controls including evaluation pipelines and prompt/chain regression tests.
• Collaborate with Data Science, MLOps/DevOps, Architecture, Product, and Business teams.
• Promote continuous learning through code reviews, tech talks, and mentoring on AI engineering best practices.