[SXEIT-MM] AIAgent 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 vendors 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.

Qualifications :

• Bachelors degree in Artificial Intelligence Computer Science or a related field.
• 1 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. Youve 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. Youve 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.

Additional Information :

This position will be contracted through Boschs external vendor under a 1-year contract. Salary and benefits will be discussed during interview

Remote Work :

No

Employment Type :

Full-time