Senior AI Lead
Job reference: 22340
Date posted: ١٠/٠٨/٢٠٢٦
Job Type: Permanent
Salary up to: Negotiable
Industry: Healthcare or Life Sciences
Category: IT,Engineering
Roles and Responsibilities
As an AI Engineer, your primary responsibilities will include:
● Own the research-to-production loop for our agent: survey emerging agentic techniques, run structured experiments against them, and ship what proves out.
● Continuously upgrade core agent capabilities — planning, tool selection and reasoning, memory, multi-agent coordination — measured against real tasks and bench outcomes, not demos.
● Build and own the evaluation harness that tells us whether a change is genuinely an improvement. For a system answerable to experiments, this is the backbone of the role.
● Diagnose where the agent fails at the edge of its current capability, and design the fixes.
● Help lead the agent platform team: set the technical direction for agentic practice internally, review and mentor, and raise the quality bar for the engineers you work alongside.
● Translate frontier research into pragmatic engineering calls under real reliability, latency, and cost constraints.
Qualifications
Required
● PhD in Computer Science, Machine Learning, Artificial Intelligence, or a highly quantitative field with a strong focus on autonomous systems or NLP.
● A senior track record building production agentic systems — real planning, tool use, and multi-step reasoning, not prompt pipelines. Depth counts more than title or exact tenure (roughly 6+ years of software/ML work, or equivalent research-and-shipping depth).
● Demonstrated ability to take a technique from current research and land it in a production system, with measured impact.
● Deep command of how agents actually work — tool definition, state and context management, orchestration, multi-step control — and the judgment to choose or build the right pattern. (LangGraph, MCP, and whatever replaces them are tools, not the skill.)
● Rigorous about evaluation: you've designed eval harnesses for non-deterministic systems and you trust data over intuition.
● Fluent reading and dismantling ML/agent papers; strong Python and system-design fundamentals.
● The judgment and communication to help lead a technical team — set direction, review others' work, raise the bar — whether or not you've carried a formal lead title.
Preferred
● Exposure to scientific or research workflows, ideally drug discovery or another iterative experimental loop.
● Prior experience mentoring or setting technical direction for a small team.
● Background in RAG, memory systems, or multi-agent orchestration; bio/chem familiarity a plus.
As an AI Engineer, your primary responsibilities will include:
● Own the research-to-production loop for our agent: survey emerging agentic techniques, run structured experiments against them, and ship what proves out.
● Continuously upgrade core agent capabilities — planning, tool selection and reasoning, memory, multi-agent coordination — measured against real tasks and bench outcomes, not demos.
● Build and own the evaluation harness that tells us whether a change is genuinely an improvement. For a system answerable to experiments, this is the backbone of the role.
● Diagnose where the agent fails at the edge of its current capability, and design the fixes.
● Help lead the agent platform team: set the technical direction for agentic practice internally, review and mentor, and raise the quality bar for the engineers you work alongside.
● Translate frontier research into pragmatic engineering calls under real reliability, latency, and cost constraints.
Qualifications
Required
● PhD in Computer Science, Machine Learning, Artificial Intelligence, or a highly quantitative field with a strong focus on autonomous systems or NLP.
● A senior track record building production agentic systems — real planning, tool use, and multi-step reasoning, not prompt pipelines. Depth counts more than title or exact tenure (roughly 6+ years of software/ML work, or equivalent research-and-shipping depth).
● Demonstrated ability to take a technique from current research and land it in a production system, with measured impact.
● Deep command of how agents actually work — tool definition, state and context management, orchestration, multi-step control — and the judgment to choose or build the right pattern. (LangGraph, MCP, and whatever replaces them are tools, not the skill.)
● Rigorous about evaluation: you've designed eval harnesses for non-deterministic systems and you trust data over intuition.
● Fluent reading and dismantling ML/agent papers; strong Python and system-design fundamentals.
● The judgment and communication to help lead a technical team — set direction, review others' work, raise the bar — whether or not you've carried a formal lead title.
Preferred
● Exposure to scientific or research workflows, ideally drug discovery or another iterative experimental loop.
● Prior experience mentoring or setting technical direction for a small team.
● Background in RAG, memory systems, or multi-agent orchestration; bio/chem familiarity a plus.
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