[SX/EIT-MM] AI/Agent Engineers (1-year contract)

• Architect agentic systems  — plan, memory, tool use, multi-agent delegation, evaluation loops, guardrails. Pick the right abstraction for the problem, not the one on the hype curve. • Push model capability into production.  Design prompt and context strategies, tool interfaces, retrieval and reranking, structured output, streaming, and evaluation — across text and multimodal inputs (vision, documents, audio). • Own the evaluation story.  Build offline eval sets, online LLM-as-judge loops, regression harnesses. Know the difference between a metric that moves your users and a metric that moves only your dashboard. • Squeeze the system.  Prompt caching, batching, speculative decoding, model routing, token budget management, latency targets. Know your P50/P99 and why they look the way they do. • Contribute upstream.  Read SDK source when docs are thin, open PRs against open-source agent frameworks, write crisp bug reports when a vendor's orchestration service returns a weird 500. • Mentor and set the bar.  Your design reviews, code reviews and technical writing shape how the rest of the team thinks about agents. • Bachelor’s degree in Artificial Intelligence, Computer Science, or a related field. • 0-3 years of experience as an AI/AI Agent Engineer. • Solid ML fundamentals.  You can explain transformers — attention, positional encoding, KV cache, tokenisation, sampling — without hand-waving. You've read at least a few core papers in full, not just the abstracts. • Deep LLM application experience.  Multiple production systems built on top of frontier models (Anthropic, OpenAI, Gemini, open-weight). You know the practical edge cases: tool-use stability, structured-output failure modes, long-context degradation, prompt-injection defence, cost control. • Agent systems depth.  You've built something with real agent behaviour — planning, memory, tool orchestration, multi-step execution, error recovery — not a single prompt in a loop. Experience with multi-agent coordination (delegation, sub-agent protocols, MCP-style tool servers) is a strong plus. • Multimodal experience.  Hands-on work with vision-language models, document AI (OCR, layout, tables), or audio — end-to-end from ingest to grounded output. • Strong engineering craft.  Python at a senior level — async, typing, testing, packaging, observability. Able to read and navigate a large codebase. Git hygiene that makes reviewers' lives easier. • Production AI engineering.  You can take ML, LLM, embedding, vision, or multimodal models from prototype to production — designing APIs and inference services, building RAG/embedding pipelines, containerizing workloads, handling CPU/GPU deployment, and optimizing latency, throughput, reliability, and cost. • AI platform & MLOps experience.  Hands-on experience operating AI workloads in production with model/version management, CI/CD, evaluation gates, observability, autoscaling, rollback, and failure handling. Experience with Docker, Kubernetes/AKS, Azure AI services, GPU inference, or model-serving frameworks such as vLLM or NVIDIA Triton is a strong plus. • Fluent with modern coding agents.  You use Claude Code / Cursor / Copilot / equivalents daily as a force multiplier. You understand where they shine and where they fail, and you can design prompts, context and tool boundaries to get the most out of them. • Communication.  Writes and speaks clearly in English. This position will be contracted through Bosch’s external vendor under a 1-year contract. Salary and benefits will be discussed during interview