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
