Design, evaluate, and ship AI that powers products used by hundreds of thousands of users across the portfolio.
End-to-end role: collaborate with product and brand teams to translate user needs and data into solution designs, plan and run experiments with clear success metrics, and deliver reliable systems to production with support from DevOps.
Work across conventional ML and modern GenAI/LLMs, selecting the right approach and caring about data quality, rigorous evaluation, and responsible use.
Help build reusable AI capabilities - services, libraries, and patterns - that scale across brands.
Ship a real feature in first 3 months and extract a reusable template; create internal case study and demo.
Requirements
Proven end-to-end delivery - you have shipped AI features to production and owned the lifecycle from problem framing to post-launch iteration.
Strong Python engineering - 5+ years building production software, with APIs (mostly FastAPI), testing, documentation, and code reviews.
Applied AI expertise - hands-on with conventional ML and GenAI/LLMs; experience integrating multiple LLM providers, working with vector databases, and using frameworks like LangChain.
Data & evaluation competence - solid SQL skills, ability to build simple pipelines, define success metrics, design experiments, and make decisions from results.
Production readiness - Docker fluency, CI/CD awareness, logging and monitoring basics, and good habits for versioning prompts, models, and datasets.
Collaboration and leadership in practice - clear communication with stakeholders, thoughtful documentation, and willingness to mentor junior colleagues.