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)