Machine Learning Engineer II ( Growth & ML), Hyderabad

Overview

Hands-on Machine Learning Engineer II role on the Data & Audience Platform team in Hyderabad, building and maintaining production ML pipelines and contributing to model development alongside Senior and Staff engineers.

What you'll do

• Build and maintain end-to-end ML pipelines for training, evaluation, and batch inference across use cases such as identity resolution, audience segmentation, and content affinity modeling.
• Implement and experiment with supervised, unsupervised, and ranking models in Python (scikit-learn, XGBoost/LightGBM, PyTorch).
• Engineer features from first-party viewership, engagement, subscription, and behavioral signals, guarding against data leakage, collinearity, and training/serving skew.
• Run structured offline experiments; evaluate with the right metrics (precision/recall, F1, AUC-ROC, calibration, lift) and document findings in MLflow.
• Develop and maintain data and feature pipelines on Databricks (PySpark, Delta, Workflows) that feed the feature store and model-training workflows, with attention to idempotency and reproducibility.
• Write clean, tested, production-quality Python following engineering best practices (unit tests, code reviews, CI/CD).
• Use MLflow for experiment tracking, model registration, and versioning under the guidance of senior engineers.
• Support deployment and monitoring of batch inference jobs integrated with downstream activation platforms (e.g., Mosaic, FreeWheel, GAM) and data in Snowflake.
• Use AI-assisted development tools (Cursor, GitHub Copilot, Amazon Q) to accelerate coding, debugging, and documentation under guidance.
• Leverage Databricks Genie for natural-language exploration of governed Unity Catalog datasets, including ML feature tables, model outputs, and audience segments.
• Use Snowflake Cortex (Copilot / Cortex Analyst) to accelerate data analysis and SQL authoring against audience and identity schemas.
• Learn and apply prompt-engineering patterns for LLM-assisted data exploration and feature generation, and participate in evaluating MCP (Model Context Protocol) tooling.
• Partner with Senior and Staff MLEs on system-design decisions and technical discussions.
• Work cross-functionally with Data Engineering, Feature Engineering, and Analytics to ensure data quality and pipeline reliability.
• Document models, pipelines, and experiments clearly for team knowledge sharing.

What you'll need

• 2–4 years of industry experience in machine learning, data science, or ML engineering (or 1–2 years with a relevant M.S.).
• Strong Python proficiency, with experience in pandas, NumPy, and scikit-learn.
• Experience with at least one deep-learning framework (PyTorch or TensorFlow).
• Hands-on experience with Spark/PySpark or equivalent large-scale data processing.
• Proficiency in SQL and familiarity with cloud data warehouses/lakehouses (Snowflake or Databricks).
• Experience with experiment-tracking tools (MLflow, Weights & Biases, or similar).
• Solid grasp of core ML concepts: classification, regression, ranking, embeddings, and model evaluation.
• Strong CS fundamentals (data structures, algorithms, clean code).
• Bachelor's degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience).
• Ability to use AI tools to independently improve productivity across the ML lifecycle.
• Clear written and verbal communication.

Nice to have

• Experience with Databricks (notebooks, Delta Lake, Workflows/DLT, Unity Catalog, MLflow).
• Exposure to recommendation systems, audience segmentation, identity resolution, or forecasting.
• Familiarity with AWS services (SageMaker, S3, Lambda) and/or feature stores (Databricks Feature Store, Feast).
• Exposure to agentic AI tools: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, or MCP.

Details

• Location: Hyderabad.