AI Engineer building scalable applications using AI tools and guiding teams to adopt AI effectively. Innovating engineering culture and workflows in an established Fortune 500 company.
About the role
- Lead AI Engineer creating and shipping production-grade AI products at Reward Gateway | Edenred. Leading a small team and driving AI productivity while ensuring high standards in the design and delivery of solutions.
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.
Requirements
- 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)
Benefits
- 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
- Inclusive and accessible recruitment process
- Commitment to diversity and inclusion
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