Role Overview: We are hiring a Senior AI Engineer / Applied Scientist to build and scale ML systems powering ecommerce search, retrieval, and personalization. This is an 80% ML Engineering role focused on production systems, rapid experimentation, and measurable business impact. You will own end-to-end AI solutions across matching, retrieval, ranking, and GenAI-powered experiences, from prototyping to deployment at scale. Core Responsibilities Design, build, and improve ML models for: • Product matching / entity resolution • Semantic retrieval and search relevance • Ranking and recommendation systems • Develop and optimize multi-stage ranking pipelines (candidate generation → ranking → re-ranking) • Run experimentation frameworks (offline evaluation, A/B testing) to drive continuous improvement • Apply reinforcement learning / bandits for personalization and ranking optimization • Build and deploy GenAI and agentic AI systems for search, discovery, and content use cases • Productionize ML systems using Databricks-based pipelines and deploy services via AKS (Azure Kubernetes Service) • Design scalable, reliable ML infrastructure with focus on latency, throughput, and cost efficiency • Monitor model performance and continuously iterate using MLOps/LLMOps best practices • Independently scope ambiguous problems and drive them from idea → production Requirements Required Qualifications 5–8+ years of experience in ML Engineering / Applied AI Strong experience with: Search, retrieval, and ranking systems NLP / embeddings / deep learning models Experimentation and evaluation methodologies Proven track record of building production-grade ML systems Strong proficiency in Python Hands-on experience with Databricks and Kubernetes-based deployment (AKS preferred) Ability to learn quickly and operate independently in a fast-evolving AI landscape Preferred Qualifications Master’s degree in Computer Science or related field (preferably in NLP or Computer Vision) Experience with: Multi-stage ranking systems (search/recommendation engines) Reinforcement learning / bandits GenAI, LLMs, and agentic frameworks (LangChain, LangGraph, etc.) Ecommerce or marketplace domains Familiarity with: Modern data platforms (Snowflake, Data Lakes) MLOps/LLMOps tools (MLflow or similar) AI governance (evaluation, explainability, safety)