Overview In this role you design, build, and operate AI-powered automation across products and platforms, shaping engineering workflows with governance and best practices. You'll lead technical direction, collaborate with cross-functional teams, and mentor engineers to deliver production-grade AI-enabled solutions. The work supports scalable, reliable AI features with a focus on developer productivity and responsible AI adoption. You join a team driving innovative automation in a medical device-oriented software environment.
Responsibilities- Lead the technical design and implementation of AI automation initiatives
- Collaborate with Product, UX, QA, Security, and Cloud Operations teams
- Influence architecture and engineering practices through reviews and guidance
- Mentor engineers via code reviews and knowledge sharing
- Design AI-enabled automation for parts of the software development lifecycle
- Build RAG pipelines, lightweight intelligent agents, and MCP-style integrations
- Integrate AI-assisted code generation and validation into CI/CD with guardrails and feature flags
- Ensure human-in-the-loop, provenance, least-privilege access; manage embeddings/logs security
- Prepare runbooks, dashboards, alerts; participate in incident responses and post-incident reviews
- Monitor reliability, latency, correctness, and cost of AI features; manage model drift
- Continuously evaluate new AI technologies to improve productivity and quality
- Define and enforce AI governance, data handling and regulatory traceability
- Establish testing, documentation, and observability for features
Key requirements- 8+ years of software engineering experience
- Proven track record delivering production-grade systems and leading complex initiatives
- Hands-on experience integrating AI components into engineering workflows (RAG, embeddings, vector stores, lightweight agents)
- Experience with distributed systems, APIs, and service-level design
- Experience with operational readiness, incident response, and reliability practices
- Strong system and service design skills with scalability and cost trade-offs for AI
- Cloud platforms (AWS/Azure/GCP), Docker, Kubernetes exposure
- CI/CD tooling (GitHub Actions/Jenkins/GitLab), feature-flagging, and Infrastructure-as-Code (Terraform/ARM)
- Knowledge of RAG pipelines, embeddings/vector DBs, prompt engineering, and retrieval-quality evaluation
- Testing and observability discipline (logs, metrics, traces)
- Security and data privacy awareness in embeddings/logs handling
- Excellent communication and stakeholder engagement skills
- German language proficiency is a plus
- Regulated-industry (medical device) experience is a plus
- collaboration
- stakeholder engagement
- effective communication
- AI-enabled automation
- RAG pipelines
- embeddings/vector databases
