Senior AI Engineer

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)