Requirements

• Experience shipping AI or ML systems that real users depend on, with close involvement in production behavior and troubleshooting.
• Strong proficiency in Python and SQL, with practical use in data assembly and analysis.
• Deliberate investment in software engineering skills, including testing, error handling, CI/CD, observability, and MLOps practices.
• Hands-on experience across the GenAI stack: LLM APIs, retrieval and vector stores, agents with tool use, structured outputs, and cloud infrastructure (AWS and GCP).
• A clear perspective on evaluation methods for AI systems, understanding pitfalls of systems that appear fine but are not.
• Ability to reason about model failures such as hallucinations and prompt injection and their impact on output trustworthiness.
• Judgment on when to use complex models versus simpler solutions or stopping development.
• Constructive reviewer who improves colleagues through code reviews and design feedback.

Nice to haves

• Curiosity about underlying mechanics, pursuing questions beyond first plausible answers.
• Attraction to ambiguous problems where the right solution is not yet clear.
• Honest communication about knowledge limits and early collaboration.
• Accountability for shipped work, monitoring performance, and willingness to fix issues.
• Appreciation for essential but unglamorous work that ensures AI feature quality beyond impressive demos.
• Ability to engage in healthy disagreement and commit once decisions are made.

What you'll be doing

• Owning AI features end-to-end from ambiguous problems to reliable, observable systems used by real customers.
• Building retrieval and data foundations with emphasis on retrieval quality as a core engineering challenge.
• Designing evaluation and monitoring strategies to measurably ensure quality, reliability, and safety.
• Developing AI applications and agents using LLMs, orchestration, APIs, data systems, and strong production software practices.
• Making decisions about models, costs, and latency that affect running costs, with clear documentation for team transparency.
• Rapid prototyping, identifying reusable elements, and formalizing effective patterns into widely adopted components.
• Raising team quality through code reviews, design feedback, and setting technical standards by example.
• Communicating technical decisions directly to product managers and stakeholders, translating model behavior into business impact.

Perks and benefits

• Competitive salary and equity scheme allowing sharing in company success.
• 25 days holiday and flexible working including 2 days a week in the London office with socials and snacks.
• Private medical insurance via BUPA and life assurance.
• Pension scheme with 5% contribution from the company.
• Enhanced family leave (e.g., 26 weeks full pay for maternity or adoption).
• 24/7 Employee Assistance Programme for support.
• EV leasing scheme, cycle to work scheme, and nursery salary sacrifice scheme.
• 1 paid volunteering day per year.