Engineering Team Lead – AI Applications (Life Sciences)
PatSnap · Suzhou
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Jul 27, 2026
Suzhou
Jul 28, 2026
What this job asks for AI summary
A hands-on technical lead role building AI-native applications for the life sciences industry, including agentic workflows, multi-agent systems, and LLM-powered solutions for biomedical reasoning and information extraction. The role blends individual engineering contribution — architecture, code reviews, prompt optimization — with team leadership and mentoring of a full-stack AI engineering team. Best suited to an experienced engineer-leader comfortable owning end-to-end AI application architecture.
Senior level · 5+ years
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How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (6)
No work location is specified in the posting — CBSA and state are left blank. The role may be remote or based outside the US; the US-based flag defaults to true given the ambiguity.
The role sits at the boundary between a hands-on senior IC and an engineering manager: it explicitly requires team leadership and mentoring but also emphasizes remaining 'actively involved' in architecture and code. It is classified as Software Developers (15-1252) because the primary day-to-day work described is technical design and engineering, with Computer and Information Systems Managers (11-3021) as a close runner-up.
The posting requires 5+ years of overall software/full-stack experience and 2+ years specifically delivering AI/LLM applications; the 2-year figure is attached to LLMs as the years demanded, and 5 years is captured as the overall experience minimum.
Agent frameworks (LangChain, AutoGen, CrewAI) are listed as a single interchangeable requirement in the qualifications; they are emitted as one skill with alternatives.
'At least one statically typed programming language' names no specific language and is therefore omitted from the skills list per the no-generic-concepts rule.
Cloud-native deployment appears in the qualifications section but is phrased as 'strong knowledge of … cloud-native deployment' without naming a specific platform (AWS, Azure, GCP), so it is captured as a preferred generic skill rather than a named platform.
Read the full posting
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