PolyModels Hub is hiring an Applied AI Lead to ship AI-powered product capabilities across ModelFlow. This role is primarily focused on product execution: taking AI opportunities from discovery to production—building assistants, agents, and LLM-enhanced workflows that integrate directly into the application and deliver measurable value to scientists and engineers.

You'll partner closely with product, design, engineering, and domain experts to define the right experiences, implement the supporting AI systems, and iterate based on real user feedback.

What You'll Do

AI Product Delivery (End-to-End)

Lead delivery of user-facing AI features across UI, CLI, and API surfaces—from concept through rollout and iteration
Build LLM-enabled workflows that support the modeling lifecycle (e.g., guidance, automation, recommendations, summarization, error explanation)

Applied LLM Systems (Practical, Production-Grade)

Design and implement RAG pipelines, tool/function-calling, agent workflows, and structured output patterns
Develop reusable building blocks for prompts, retrieval, orchestration, and response shaping across product areas
Own quality and safety patterns: guardrails, fallback strategies, and human-in-the-loop workflows where needed

Evaluation, Reliability, and Operating Excellence

Establish evaluation practices: offline test sets, regression suites, and online signals tied to user outcomes
Implement monitoring and observability for LLM features (quality, latency, cost, failure modes)
Drive production readiness: feature flags, staged rollouts, incident response, and continuous improvement

Cross-Functional Leadership

Translate product goals into technical plans and clear delivery milestones
Work across modeling, data/ontology, and frontend layers to integrate AI into core workflows
Align stakeholders on tradeoffs across speed, quality, interpretability, and cost

Requirements

What we're looking for

Core Qualifications

5+ years building and shipping ML/LLM-enabled systems in production, with clear ownership of delivery outcomes
Hands-on experience with modern LLM approaches: RAG, agent orchestration, prompt design, evaluation, and deployment
Strong engineering foundation in system design, API design, and reliability (latency, scalability, and operational maturity)
Product mindset: you can shape ambiguous ideas into shippable increments and iterate based on user feedback

Nice to have

Experience with ML lifecycle or model-serving tooling (e.g., MLflow)
Exposure to scientific/structured data, data modeling, or ontology-driven platforms
Familiarity with workflow engines and domain-heavy platforms (pharma/biotech/materials)

Benefits

Flexible hybrid working (home + London office)
26 days annual leave + flexible bank holidays
Modern workspace perks
Competitive pension contributions
Equity incentives via our Employee Incentive Plan

PolyModels Hub

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