ai engineer for enterprise AI solutions

Bayer is a chemical, pharmaceutical, and biotechnology company focused on solving global challenges in health and food security. Its Enterprise Data & Analytics Platform develops AI solutions for Finance, Supply Chain, HR, Procurement, Legal, and Communications.

• Industrialize and scale successful 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 with OpenAPI/gRPC;
• Develop Model Context Protocol servers with FastMCP to safely expose enterprise tools and data to agents, ensuring robust permissions and auditing;
• Architect agent workflows with LangChain, LangGraph, and PydanticAI, including tool calling, memory, and event-driven orchestration;
• Build reliable text-to-SQL solutions and/or RAG services with embeddings, indexing, reranking, and caching;
• Implement CI/CD pipelines with GitHub Actions and automated testing;
• Deploy AI workloads on AWS and/or Azure using containers, serverless services, API gateways, managed databases, object storage, and secrets management;
• Ensure end-to-end observability through structured prompt and response logging with redaction, token, latency and cost tracking, OpenTelemetry tracing, and model and agent monitoring;
• Establish safety and quality controls, including evaluation pipelines, prompt and chain regression tests, content guardrails, and injection defenses;
• Collaborate across Data Science, MLOps/DevOps, Architecture, Product, and Business to align solutions with outcomes;
• Contribute to stack decisions and cost and scalability trade-offs;
• Promote continuous learning through code reviews, tech talks, and mentoring on AI engineering best practices.

• Master’s degree or equivalent in Computer Science, Data/AI, Mathematics, or a related field;
• 5+ Years of professional experience in AI, software, or ML engineering;
• End-to-end product delivery experience in production environments;
• Advanced Python and production-grade REST/gRPC API development skills;
• Experience with authN/authZ, OAuth2/OIDC, and rate limiting;
• Containerization expertise with Docker;
• Proficiency in AWS and/or Azure;
• Strong CI/CD knowledge, especially GitHub Actions;
• Infrastructure as Code experience; Terraform is preferred;
• Solid understanding of LLMs and embeddings, including context management, tool calling, streaming, and latency and cost trade-offs;
• Strong software engineering fundamentals, including testing, code reviews, error handling, and reliability/resilience;
• Excellent problem-solving and communication skills;
• Fluent English, written and spoken;
• Nice to have: PhD, Kubernetes experience, hands-on experience with LangChain, LangGraph, PydanticAI, FastAPI and FastMCP, RAG and vector search proficiency with pgvector or OpenSearch, relational database experience with PostgreSQL, Databricks experience.

• Variable pay components, including performance-based bonuses, are available;
• Medical care above statutory requirements;
• Flexible benefits supporting leisure and well-being, including sports programs;
• Life, accident, and disability insurance through group coverage;
• Employer-supported pension plans with regular company contributions;
• Home office allowance;
• Extra paid holidays;
• The hybrid model can be discussed with the manager.