TL;DR

Applied AI Lead (AI): Building and optimizing AI-powered product capabilities across ModelFlow with an accent on product execution from discovery to production. Focus on designing and implementing LLM-enabled workflows, RAG pipelines, and ensuring system reliability and scalability.

Location: Hybrid (London, UK)

Company

%hirify_global% is a company focused on shipping AI-powered product capabilities across ModelFlow to scientists and engineers.

What you will do

Lead end-to-end delivery of user-facing AI features across UI, CLI, and API surfaces.
Build LLM-enabled workflows that support the modeling lifecycle (e.g., guidance, automation, recommendations).
Design and implement RAG pipelines, tool/function-calling, agent workflows, and structured output patterns.
Establish evaluation practices (offline test sets, regression suites) and implement monitoring for LLM features.
Translate product goals into technical plans and clear delivery milestones.
Work across modeling, data/ontology, and frontend layers to integrate AI into core workflows.

Requirements

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.
Product mindset to shape ambiguous ideas into shippable increments and iterate based on user feedback.
Work format: Hybrid, requiring presence in London office.

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).

Culture & Benefits

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