Staff Machine Learning Engineer (Data & Audience Platform Team), Hyderabad
On-site · Hyderabad, Telangana, India
Job Summary
Staff Machine Learning Engineer (Data & Audience Platform) in Hyderabad leads the architectural direction for Hyderabad ML capabilities, owning production ML data pipelines, model training/serving, MLOps, and cross-functional collaboration. Responsible for designing and implementing scalable ML systems spanning identity intelligence, audience intelligence, content affinity, and forecasting, while driving standardization, ML principles, and governance across the team. Expected to influence architectural decisions, champion agentic AI workflows, integrate Databricks-first infrastructure with Snowflake and AWS SageMaker, and mentor senior engineers to deliver high-impact ML solutions at scale.
Required Qualifications
- 8+ years of industry experience in ML engineering (6+ with a Ph.D.) with demonstrated Staff-level scope and impact
- Mastery of the full ML stack: data engineering, feature engineering, model development, MLOps, and production monitoring
- Deep Databricks expertise: Delta Lake, Unity Catalog, Workflows/DLT, MLflow, Feature Store, Asset Bundles, Genie Space
- Strong AWS proficiency (SageMaker training/pipelines/model registry, S3, Lambda, Glue) and Snowflake expertise (DCR patterns, Snowpark, Cortex)
- Proven experience architecting production ML systems serving millions of users, and a track record of technical leadership (setting standards, driving architecture, influencing across teams)
- Expert proficiency with ML frameworks (PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn) and deep understanding of statistics and ML fundamentals
- Master’s or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field (or equivalent industry experience)
- Excellent communication, with the ability to advocate technical solutions to engineering, science, product, and executive audiences
- Preferred: Streaming / media / ad-tech ML: identity resolution, audience modeling, recommendation/ranking, content understanding
- Hands-on experience with agentic AI frameworks at production scale (LangGraph, AutoGen, MCP, CrewAI), Databricks Genie Space curation, and Snowflake Cortex Search / Fine-Tuning
- Experience with real-time feature serving and low-latency inference, and with mixture-of-experts or graph neural networks
Additional Requirements
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