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)