Lead AI Engineer
Reward Gateway | Edenred is a global leader in benefits and employee engagement. We help businesses attract, engage, and retain top talent through strategic rewards, recognition, and well‑being solutions.
Due to continued business growth, we are looking for a Lead AI Engineer who will report to the Director of AI Engineering. 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. You will architect and build production‑ready AI systems, guide technical decision‑making, and ensure we deliver meaningful products for our clients and employees.
Key Responsibilities
* 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.
Skills, Knowledge and Expertise
* 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.
Nice‑to‑have
* Advanced retrieval/reranking and context/chunking strategies; offline/online evaluation frameworks.
* ML data lifecycle: feature engineering; train/val/test splits; dataset versioning; pipeline orchestration; production monitoring for drift/quality.
* Ability to contribute to React, TypeScript, and PHP codebases.
* Cost and performance at scale: model routing, caching, token/latency budgets; SLOs (e.g., p95 latency, availability).
The Interview Process
* 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.
Equal Opportunity Employer
We are committed to ensuring an inclusive and accessible recruitment process for all candidates. If you have any specific requirements or need reasonable adjustments at any stage of the recruitment journey, please let your Talent Acquisition Partner know. Your needs are important to us, and we want to ensure an equitable experience for every candidate.
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