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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)