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.

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)

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

Lead AI Engineer

Key skills