Senior Machine Learning Engineer (Data & Audience Platform Team), Hyderabad
On-site · Hyderabad, Telangana, India
Job Summary
Senior Machine Learning Engineer (Data & Audience Platform) in Hyderabad responsible for the design and delivery of production ML systems that impact audience targeting, advertising revenue, subscriber engagement, and retention. Lead end-to-end ML workflows including probabilistic identity resolution, lookalike modeling, single-title affinity, and forecasting. Architect scalable data pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) and Snowflake, integrate with activation systems, and drive experimentation (offline/online, AUC-ROC, NDCG, calibration) with causal/inferential techniques. Mentor MLEs, collaborate with Product, Marketing, and Ad Sales to translate business requirements into ML problems, and advance MLOps practices (model versioning, retraining triggers, drift detection, monitoring) while leveraging agentic AI tools (LangChain, LangGraph, MCP) and Databricks Genie/Snowflake Cortex for governance and self-service analytics.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience)
- 5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.)
- Deep Python expertise and production-quality software engineering practices
- Production experience building and deploying ML at scale (millions+ of users/records)
- Strong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog)
- Solid SQL/Snowflake experience for feature sourcing and model-output delivery
- Experience with AWS ML services (SageMaker, S3, Lambda)
- Strong understanding of ML model evaluation, A/B testing, and statistical inference
- Ability to lead technical decisions and mentor engineers
- Familiarity with Data Clean Room environments (Snowflake DCR, AWS Clean Rooms) and consent-aware activation
- Experience with two-tower / retrieval architectures, probabilistic identity resolution, and probability calibration
- Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP)
- Databricks Genie Space and Snowflake Cortex familiarity
- Experience with feature stores (Databricks Feature Store, Tecton, Feast)
- Contribution to open source or ML publications are a plus
- Preferred: recommendation systems, personalization, identity resolution, or audience modeling in media/ad-tech context
Additional Requirements
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