Lead Engineer, Machine Learning

Sephora is a leading beauty retailer that values diversity and inclusivity. They are seeking a Lead Machine Learning Engineer to drive the architecture, engineering, and deployment of AI/ML systems, impacting customer experiences and product offerings in the beauty industry.

Responsibilities:

• Architect & Engineer Production-Grade AI/ML Systems. Design, build, and maintain scalable ML and Agentic AI systems using established engineering design patterns. Lead security-first and reliability-first practices, maintain deep domain expertise in ML systems and LLM infrastructure, and proactively anticipate future technical needs, scalability requirements, and cost implications
• Own End-to-End ML Solutions. Engineer and own batch and real-time model serving, agentic pipelines, RAG systems, and LLMOps infrastructure. Build and maintain robust tooling for monitoring, observability, logging, automated testing, performance testing, and A/B experimentation to ensure production reliability and continuous improvement
• Establish & Optimize ML Pipelines. Build scalable, efficient, and automated pipelines for data processing, feature engineering, model development, validation, evaluation, and deployment — ensuring reproducibility, quality, and operational excellence across the full ML lifecycle
• Deliver High-Quality Code in a Continuous-Release Environment. Write clean, efficient, and well-structured code to deliver AI/ML products iteratively. Uphold high engineering standards including code reviews, CI/CD integration, and test coverage across ML services and agentic workflows
• Partner Cross-Functionally to Shape AI/ML Capabilities. Collaborate closely with Product, Engineering, Data Scientists, ML Engineers, and Business stakeholders to define, scope, and plan new AI/ML capabilities — translating business requirements into technically sound, scalable engineering solutions
• Drive Delivery Planning & Engineering ROI. Review and prioritize epics and projects with clear breakdown, dependency management, and delivery planning. Proactively identify, communicate, and resolve blockers or delays. Navigate ambiguity and high-pressure situations with decisiveness, applying economic thinking to maximize value delivery
• Mentor, Grow & Inspire the Team. Mentor and develop ML Engineers and Data Scientists by promoting best practices in ML engineering, code quality, and operational excellence. Foster a culture of effective communication, continuous feedback, and knowledge sharing. Build strong cross-functional relationships and actively contribute to engineering strategy and the AI/ML product roadmap

Requirements:

• 5+ years hands-on experience in model development, training pipelines, feature stores, model serving, and MLOps/LLMOps — with a proven ability to take systems from experimentation to production at scale
• 8+ years proficiency in Python, distributed systems, API design, and cloud-native architectures, with a strong command of engineering best practices including CI/CD, testing, and observability
• 3+ years proven experience building and deploying LLM-powered applications, including RAG pipelines, prompt engineering, fine-tuning, and evaluation frameworks
• Hands-on experience with Agentic AI frameworks such as LangChain, LangGraph, Claude, or similar, with the ability to architect and engineer production-grade multi-agent systems
• Strong understanding of supervised/unsupervised learning, recommendation systems, reinforcement learning, and model evaluation methodologies
• Experience with Kubernetes, Docker, Databricks, MLflow, Vector databases, and cloud platforms (AWS, GCP, or Azure)
• A passion for exploring new ideas, staying current with the latest advancements in AI/ML, and solving complex engineering challenges at scale — bringing those insights back to elevate the team
• Excellent communication skills with the ability to align stakeholders, influence technical direction, and drive clarity across engineering, product, and business teams