Senior AI Engineer

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)