Senior AI Engineer (all genders)

You will be a senior individual contributor working directly with the founders. The person in this role won’t be handed a roadmap - they’ll help define it, and build the AI practice around it.

What you will do

AI Ownership

• Own the agent layer end-to-end: prompt and context design, tool use, orchestration, evals, and long-term technical direction.
• Be the person who understands why an agent made the decision it made - and who other engineers come to when a model behaves in ways they can’t explain.
• Set the bar on how we build with LLMs: eval-driven development, context engineering, structured outputs, cost and latency discipline.
• Make foundational decisions with conviction - which model, which pattern, which guardrail - document the reasoning, and evolve them as the system grows.

LLM Systems in Production

• Design agents that survive real enterprise environments: ambiguous inputs, flaky upstream systems, exception cases that weren’t in anyone’s spec.
• Build the eval harness that lets us ship confidently: offline evals, online evals, regression suites, human-in-the-loop review.
• Think in failure modes: hallucinations, silent drift, prompt injection, tool misuse. Build the detection before you need it.
• Own cost and latency as first-class product requirements, not afterthoughts.

Agent & Context Engineering

• Design agent architectures that compose cleanly: tool selection, state, memory, planning, escalation.
• Build the context layer that makes agents actually useful on top of SAP and Oracle data - retrieval, grounding, schema awareness, auditability.
• Evolve agents without breaking live customer deployments; manage prompt and model versioning with intention.
• Know when the right answer is a bigger model, a better prompt, a fine-tune, a tool, or just classical code.

Reliability & Systems Thinking

• Detect regressions in agent behavior before a customer does; build the evals and observability to catch them early.
• Own the operational posture of the AI systems you ship: alerting, runbooks, replay, on-call readiness.
• Identify structural debt in prompts, pipelines, and context before it compounds; refactor with thought.

What we are looking for

• Experience: 6+ years building production software, with clear ownership of LLM-powered systems in real customer environments - not demos, not notebooks.
• LLM depth: Non-negotiable. You’ve shipped agents or LLM features that run on live traffic. You have strong opinions about prompting, context, tool use, structured outputs, and what breaks in production.
• Evals as a first instinct: You don’t ship LLM code without a way to measure it. You’ve built eval pipelines that actually catch regressions, not just vibes.
• Python depth: You’re fluent in Python and comfortable in production service codebases. You make deliberate calls about async, framework choice, and performance.
• Reliability instinct: Hallucination containment, idempotency on tool calls, graceful degradation when a model is wrong - part of how you design, not patched in later.
• End-to-end ownership: You’re comfortable being the person who holds the full agent picture - from prompt to production incident. You identify what’s broken or missing before you’re asked.
• A real spike: We’re a strong team with broad coverage. What we’re hiring for is someone who brings a 10x improvement in an area we’re currently underinvested in. That might be agent evaluation, context engineering on enterprise data, agentic reliability, deep NLP/LLM internals, or domain-specific grounding. We don’t know exactly what shape it takes - but you should know what yours is, and be able to articulate why it matters.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation