Build and scale production machine learning systems for ecommerce search, retrieval, ranking, recommendations, personalization, and GenAI experiences. Responsibilities include developing ML models, optimizing multi-stage ranking pipelines, running offline and A/B experiments, applying reinforcement learning and bandits, deploying Databricks pipelines through AKS, designing scalable ML infrastructure, and monitoring models using MLOps and LLMOps practices.
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)
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