Senior Applied AI Engineer (all genders)
Kronberg, Campus Kronberg 1
Posted 21d ago
About the job
You will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. This role involves working directly with client engineering teams, leading technical design sessions, and building reusable patterns and accelerators that scale beyond individual engagements. You will gain breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside leading AI companies and a direct pathway to advanced engineering programs.
Responsibilities
- Design and build production-grade agentic systems end-to-end, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
- Build and own RAG pipelines, focusing on embeddings, chunking strategy, vector search, and context window engineering.
- Integrate and abstract across multiple LLM providers (e.g., OpenAI, Anthropic, Vertex AI, open-source models) with fallback routing, token, cost, and latency management.
- Implement LLMOps in production, including eval harnesses with real quality metrics, prompt versioning, and observability tooling.
- Embed directly with client engineering teams to design, prototype, and deploy agentic solutions through workshops, proofs of concept, and code-with sessions.
- Build reusable patterns, accelerators, and playbooks to scale solutions and enable faster starts for future engagements.
- Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness, and present findings to client stakeholders.
- Debug and provide observability for production systems.
Requirements
- Extensive years of software engineering experience in production environments.
- Hands-on experience designing and deploying agentic AI solutions in a production environment.
- Demonstrated experience with agentic orchestration frameworks like LangGraph, CrewAI, or AutoGen at a production depth.
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction and token/cost/latency management.
- Experience with RAG pipeline ownership, including embeddings, chunking strategy, vector databases, and context engineering.
- Understanding of LLMOps fundamentals, including eval harness design, prompt versioning, and production observability.
- Cloud-native engineering maturity, including Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
- Strong Python skills; Java or equivalent backend language is acceptable.
- Very good German and English language skills.
- Willingness to travel occasionally for business purposes.
Benefits
- Opportunity to architect and deliver AI-powered solutions for leading global enterprises.
- Access to cutting-edge AI ecosystems and strategic technology partnerships.
- Collaboration with highly experienced engineering teams.
- Clear pathways to technical leadership, specialist career tracks, mentoring, and continuous professional development.
- Flexible working models and hybrid work options.
- Competitive rewards and additional financial benefits, including bonus programs and employee share purchase opportunities.