Responsibilities:

• Drive the architecture, development and optimization of (generative) AI applications with a strong focus on
model evaluation, retrieval-augmented generation (RAG), and prompt engineering.
• Implement end-to-end RAG pipelines (ingestion, chunking, embeddings, retrieval, re-ranking) and integrate them
with internal data sources.
• Design and integrate data storage for AI applications using vector search in Snowflake and relational databases
such as PostgreSQL.
• Analyze and integrate data from various sources, ensuring high data quality and preparing it for effective AIdriven solutions.
• Experiment with LLM models (e.g. OpenAI, Anthropic, Azure OpenAI) to improve performance, reliability and
applicability across business use cases.
• Establish and maintain robust engineering practices for GenAI applications (testing, observability, security,
performance optimization).
• Collaborate closely with our AI platform team to enhance, standardize and scale AI-driven capabilities across the
BMW Group.
• Work closely with different business units, helping them leverage AI models for real-world applications and
continuous improvements.
• Share your expertise in Generative AI, LLM orchestration, and software engineering with other teams,
contributing to knowledge-sharing and innovation within the organization.

Qualifications:

• University degree in Computer Science, Engineering, or a related field.
• At least 7 years of professional software engineering experience, with a strong focus on backend or dataintensive applications.
• Strong proficiency in Python and experience building production systems.
• Strong understanding of software development best practices (clean code, unit/integration testing, code
reviews, CI/CD).
• Hands-on experience with LLM APIs (e.g. OpenAI, Anthropic, Azure OpenAI) and LLM orchestration frameworks
such as LangGraph.
• Proven experience with RAG architectures and vector databases / embeddings.
• Experience with data ingestion and preprocessing for LLM applications (ETL, data pipelines, document
processing).
• Experience implementing guardrails, safety checks and evaluation frameworks for LLM applications.
• Familiarity with MLOps or LLMOps concepts and best practices for deploying, monitoring and maintaining AI
models and AI-enabled services in production.
• Strong problem-solving skills and the ability to work in a collaborative, agile environment.
• Excellent English communication skills, both written and spoken.