Sephora aims to redefine the beauty experience through AI/ML initiatives, leveraging technologies like generative AI, reinforcement learning, and RAG-based knowledge bases to enhance customer discovery and personalization in the beauty space.
Requirements
- 5+ years experience developing and deploying machine learning systems into production
- 8+ years experience in Software Engineering
- 2-4 years experience working with AI Agentic systems, LLMs, and RAG architecture
- Experience working with MCP (Model Context Protocol)
- Experience using open source LLMs and LLMOPs
- 3-5 years experience working with a variety of relational SQL and NoSQL databases
- Experience working with: Spark, Kafka, Scala, Python, etc.
Responsibilities
- Architect, build, maintain scalable systems using established design patterns, leads security-first practices, and maintains deep domain expertise while anticipating future technical needs and costs
- Implement end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, performance testing and A/B testing
- Collaborate with Product, Engineering, Data Scientists, ML Engineers and Business teams on planning new capabilities
- Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation
- Write efficient and well-organized software to ship products in an iterative, continual-release environment
- Reviews and prioritizes epics/projects with proper breakdown and dependency management, proactively identifies and communicates blockers or delays, handles uncertainty and high-pressure situations decisively, and applies economic thinking to optimize value delivery
- Mentor teammates to adopt best practices in writing and maintaining production machine learning code and growth opportunities, fosters cultures of effective communication, feedback, and knowledge sharing, builds strong cross-functional relationships, and collaborates on engineering strategy while contributing to product roadmap development
Other
- Excellent communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences
- Experience with Azure, AWS or equivalent cloud platforms
- Microsoft Azure: Experience designing, deploying, and administering scalable, available, and fault tolerant systems on Microsoft Azure
- Hands-on Experience working with Databricks
- Familiarity in deploying real-time ML systems on Azure Cloud through frameworks such as ONNX, MLEAP, TF Serving, etc.
