AI SAP System Integration Engineer (all genders)
Remote • Wuerzburg, Schuererstrasse 5a
Posted 21h ago
Job Location
Wuerzburg, Schuererstrasse 5a
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Agent Engineer
About the job
This role focuses on building production-grade AI systems for enterprise environments, specifically within manufacturing and system integration practices. You will be responsible for developing and deploying agentic architectures that solve real-world SAP, logistics, and manufacturing challenges. The position offers exposure to diverse enterprise environments and collaboration with leading AI technology providers.
Responsibilities
- Design and build end-to-end production-grade agentic systems, including multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
- Build and manage RAG pipelines, focusing on embeddings, chunking strategy, vector search, and context window engineering.
- Integrate and abstract across multiple LLM providers (OpenAI, Anthropic, Vertex AI, open-source models), managing fallback routing, tokens, costs, and latency.
- Implement LLMOps in production, including eval harnesses with real quality metrics, prompt versioning, and observability tooling (LangSmith, Braintrust, or equivalent).
- Embed with client engineering teams to design, prototype, and deploy agentic solutions through workshops, proofs of concept, and code-with sessions.
- Define and utilize metrics to measure agent accuracy, latency, safety, and cost-effectiveness, presenting findings to client stakeholders.
- Debug and monitor production systems, ensuring high availability and performance.
Requirements
- Proven experience shipping multi-agent systems in production.
- Experience owning an evaluation harness for AI systems.
- Software engineering experience in production environments, with a background in SAP, manufacturing, logistics, or system integration.
- Hands-on experience designing and deploying agentic AI solutions in production.
- 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/latency/cost management.
- Experience with RAG pipeline components: embeddings, chunking strategy, vector databases, and context engineering.
- Understanding of LLMOps fundamentals: 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.
- Production debugging and observability experience.
- Very good German and English language skills.
- Willingness to travel occasionally for business purposes.
Benefits
- Extensive learning and development opportunities.
- Dedicated mentoring and structured onboarding.
- Flexibility to work from an office, remotely, or directly with clients.