Description

You will design, train, and productionize ML models powering search relevance, retrieval, personalization, and LLM-based conversational experiences at massive scale.

Responsibilities

• Build and improve search ranking, retrieval, and query understanding models.
• Develop ML components for Conversational Search, including multi-turn context handling and intent detection.
• Design and optimize embedding models, vector stores, and similarity search systems.
• Build personalized ranking and recommendation models using deep learning.
• Implement ML pipeline best practices, including versioning, monitoring, and A/B testing.

Required Skills

• 4–10 years of experience in Machine Learning or Applied ML engineering.
• Strong foundations in ML, deep learning, Transformers, and neural retrieval.
• Hands-on experience with Search systems (retrieval + ranking) and Recommendation models.
• Proficiency with Embedding models and vector databases.
• Proven experience building production-grade ML systems at scale.
• Strong coding skills in Python and experience with modern ML stacks.
• Familiarity with LLMs, RAG architectures, prompt engineering, and agent workflows.
• Experience deploying models in distributed systems alongside backend teams.

Key Skills

Education

Any Graduate