Senior Software Engineer, Agentic AI for Automotive

Senior Software Engineer, Agentic AI for Automotive

BMW Group

Munich, Germany

What awaits you?

• You pioneer the transformation toward an AI-native Software Development Lifecycle (SDLC) and continuously evaluate rapidly advancing AI capabilities to keep BMW at the frontier.
• Furthermore, you design and build AI-driven engineering toolchains and workflows, agentic systems, and context engineering strategies.
• You build and operate AI-driven engineering environments and automated feedback loops to drive measurable improvements in developer productivity.
• You ensure system quality with evaluations, guardrails, and validation of software development artefacts and outputs.
• In addition, you enable and coach engineering teams to adopt AI-native workflows, collaborating across multiple divisions and international locations.
• You drive knowledge exchange across the developer community, for example through hackathons, workshops, and training sessions.
• You contribute to our transformation initiative, roadmap planning, cross-team alignment, and data-driven insights that guide scaling decisions.

What should you bring along?

• University degree in computer science, mathematics, software engineering, or a comparable field.
• At least five years of professional software engineering experience developing high-quality production software, ideally from concept to production (Rust, Java, Kotlin, or C++).
• Deep experience with AI coding assistants such as Claude Code, GitHub Copilot, Cursor, or similar tools, with knowledge in context engineering, agentic workflows, MCP, A2A, and agent skills.
• Hands-on experience in designing, building, and operating agentic systems (e.g., LangGraph), including evaluation and quality assurance.
• Experience with CI/CD pipelines, automation of toolchains, and containerised development environments.
• Proficiency in collaborating across multiple teams and international locations, driving adoption of new tools and workflows, and scaling across a large engineering organisation.
• Experience with data-driven decision making, defining metrics, running experiments, and turning measurements into actionable insights.