Description

Key Skills: Machine Learning, Python, AWS, Databricks, Snowflake, MLOps, PyTorch, TensorFlow, SQL, Data Engineering

Good to Have Skills: Deep knowledge of probabilistic identity resolution with 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 such as LangGraph, AutoGen, MCP, and CrewAI. 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, recommendation systems, and content understanding. Published research or conference presentations in relevant ML domains would be advantageous.

Roles & Responsibilities:

• Define and own the technical architecture for core systems including probabilistic identity spine, audience intelligence platform, and content-affinity models.
• Lead architectural decisions for MLOps framework including feature-store design, training-pipeline standards, and model-serving patterns on Databricks architecture.
• Evaluate and recommend new technologies and approaches with clear build/buy/partner assessments for the organization.
• Drive standardization of ML practices across Hyderabad and align with global WBD ML engineering standards.
• Architect and lead delivery of probabilistic identity resolution system resolving device IDs to households with calibrated confidence at scale.
• Lead the evolution of Audience Intelligence including ML Promo Optimizer and lookalike modeling inside Snowflake DCR.
• Own the ML architecture for forecasting audience growth, demand, and yield while ensuring models are production-grade and monitored.
• Define the team's MLOps target architecture including feature contracts, model-registry governance, and automated retraining processes.
• Establish engineering standards for the full ML lifecycle from data contracts to model deployment and monitoring.
• Lead the team's adoption of agentic AI development and define standards for production ML workflows.
• Serve as technical anchor for Hyderabad team providing architectural guidance and mentorship to senior engineers.
• Partner with Senior ML Engineering Manager on technical roadmap prioritization and team capability development planning.

Experience Required: 8+ years of industry experience in ML engineering with demonstrated Staff-level scope and impact. Mastery of the full ML stack including data engineering, feature engineering, model development, MLOps, and production monitoring. Proven experience architecting production ML systems serving millions of users with a track record of technical leadership.

Education: Master's or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field (or equivalent industry experience)

Key Skills

Education

Any Graduate