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- Tasks: Lead the delivery of AI features and build innovative workflows for scientists and engineers.
- Company: Join PolyModels Hub, a forward-thinking tech company focused on AI advancements.
- Benefits: Enjoy flexible working, 26 days leave, modern perks, and equity incentives.
- Why this job: Make a real impact by shaping AI products that enhance user experiences.
- Qualifications: 5+ years in ML/LLM systems with strong engineering and product mindset.
- Other info: Collaborative environment with opportunities for growth in a dynamic field.
The predicted salary is between 36000 - 60000 £ per year.
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 will 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 will 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
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
Applied AI Lead in London employer: PolyModels Hub
Contact Detail:
PolyModels Hub Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Lead in London
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or a personal project that highlights your AI capabilities. This gives you something tangible to discuss during interviews and shows you’re proactive.
✨Tip Number 3
Prepare for the interview by understanding the company’s products and how they use AI. Tailor your examples to show how your experience aligns with their needs—this will make you stand out!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are keen to join us directly.
We think you need these skills to ace Applied AI Lead in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Applied AI Lead role. Highlight your experience with ML/LLM systems and how you've successfully delivered user-facing AI features in the past. We want to see how your skills align with our needs!
Showcase Your Projects: Include specific examples of projects where you've built AI capabilities or worked on LLM-enabled systems. We love seeing real-world applications, so don’t hold back on the details—tell us about the challenges you faced and how you overcame them!
Be Clear and Concise: When writing your application, keep it straightforward and to the point. Use clear language to describe your experiences and achievements. We appreciate a well-structured application that makes it easy for us to see your qualifications at a glance.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at PolyModels Hub!
How to prepare for a job interview at PolyModels Hub
✨Know Your AI Stuff
Make sure you brush up on the latest in AI, especially around LLMs and RAG pipelines. Be ready to discuss your hands-on experience with these technologies and how you've applied them in real-world scenarios.
✨Showcase Your Product Mindset
Prepare examples of how you've taken ambiguous ideas and turned them into shippable products. Highlight your ability to iterate based on user feedback and how that has led to successful outcomes in your previous roles.
✨Cross-Functional Collaboration is Key
Think about times when you've worked closely with product, design, and engineering teams. Be ready to share how you translated product goals into technical plans and how you aligned stakeholders on trade-offs.
✨Demonstrate Your Evaluation Practices
Be prepared to discuss how you've established evaluation practices in your past projects. Talk about your experience with monitoring and observability for AI features, and how you've driven production readiness in your work.
