Description

Define and own the technical architecture for core ML systems including probabilistic identity resolution and audience intelligence platforms.

This role is on-site.

Responsibilities

• Architect and lead delivery of probabilistic identity resolution systems, mapping device IDs to households with calibrated confidence at scale.
• Lead architectural decisions for MLOps frameworks, including feature-store design, training-pipeline standards, and model-serving patterns on Databricks.
• Define the team's MLOps target architecture, establishing feature contracts, model-registry governance, and automated retraining processes.
• Drive standardization of ML practices across the Hyderabad team, aligning with global engineering standards and providing technical mentorship.
• Evaluate and recommend new technologies with clear build/buy/partner assessments for the organization.

Required Skills

• 8+ years of industry experience in ML engineering with demonstrated Staff-level scope and impact.
• Mastery of the full ML stack: data engineering, feature engineering, model development, MLOps, and production monitoring.
• Proven experience architecting production ML systems serving millions of users.
• Proficiency in Python, AWS, Databricks, Snowflake, PyTorch, TensorFlow, SQL, and Data Engineering.
• Deep knowledge of probabilistic identity resolution, entity resolution, and graph-based household matching.
• Experience with Data Clean Room ML, including Snowflake DCR and AWS Clean Rooms.
• Hands-on experience with agentic AI frameworks at production scale (LangGraph, AutoGen, MCP, CrewAI).

Preferred Skills

• Familiarity with Databricks Genie Space curation and Snowflake Cortex Search for enterprise RAG.
• Experience with real-time feature serving, low-latency inference, mixture-of-experts, and graph neural networks.
• Background in streaming, media, or ad-tech ML including identity resolution, audience modeling, and recommendation systems.

Key Skills

Education

Any Graduate