Lead AI Engineer, Reward Gateway

• This role will be a subject matter expert for applied AI within our Engineering organisation
• This role suits a hands‑on team lead who builds and ships customer‑facing, production‑grade AI, solving complex problems, shaping long‑term strategy, and rolling up their sleeves to help teams adopt practical, reliable AI every day
• Hands‑on leader who has built and shipped production AI and full‑stack systems
• Python first; testing and CI/CD; fluent with AI coding assistants (Cursor, Copilot, Claude Code) and accountable for outcomes
• Applied LLMs: Claude or similar; RAG, agents, prompt engineering; embeddings + vector DBs; hybrid search; evaluation/benchmarking
• Data engineering: ETL; SQL/NoSQL design; data sourcing/ingestion with governance and quality
• Cloud/platform: AWS, Kubernetes, Docker; infrastructure as code; observability
• Privacy and compliance in regulated settings; clear communicator; mentor who sets objectives and drives ownership

Desirable

• Due to continued business growth, we are looking for a Lead AI Engineer who will report to the Director of AI Engineering
• You will architect and build production-ready AI systems, guide technical decision-making, and ensure we deliver meaningful products for our clients and employees
• Architect, build, and ship production-grade AI / Generative AI products using LLMs, RAG, agents, strong context strategies, and responsible guardrails
• Lead a small team (initially 2 AI engineers) and scale it; partner with Product using a working backwards mindset; set objectives, run Agile/Scrum, coach, and make fast, high-quality decisions
• Drive 10x productivity with AI agents and AI coding assistants while maintaining high standards for code quality, testing, reviews, and observability, taking full ownership of AI outcomes
• Evolve POCs into measurable solutions with clear metrics, A/B tests, online evaluation, and lightweight models
• Own end-to-end workflows: data pipelines, evaluation and benchmarking, instrumentation, human-in-the-loop validation, and compliance
• Operate on AWS and Kubernetes with CI/CD, infrastructure as code, monitoring, performance, and cost control
• Make pragmatic choices, including LLMs, prompting vs fine-tuning, embeddings and vector search, hybrid search; ensure privacy, PII protection, and enterprise controls

• Online interview with the Talent Partner and the Director of AI Engineering
• Technical interview with Director of AI Engineering, VP of Product Engineering, and VP of Product