Senior Machine Learning Engineer

About TCS:

A purpose-led organization that is building a meaningful future through innovation, technology, and collective knowledge. We're #BuildingOnBelief.

Tata Consultancy Services (TCS) is a global leader in IT services, digital and business solutions that partners with its clients to simplify, strengthen and transform their businesses. TCS offers a consulting-led, integrated portfolio of IT, BPS, infrastructure, engineering and assurance services. We ensure the highest levels of certainty and satisfaction through a deep-set commitment to our clients, comprehensive industry expertise and a global network of innovation and delivery centers. For more information, visit us at www.tcs.com.

Job Description:

Key Responsibilities

• Design and develop predictive scoring models covering classification and regression use cases
• Build forecasting models for time series analysis and demand prediction
• Develop optimization models for planning, allocation and supply chain scenarios
• Develop feature engineering pipelines for large-scale structured and semi-structured data
• Build end-to-end pipelines using Databricks notebooks, workflows and job orchestration
• Implement Spark-based distributed processing for feature engineering and training pipelines
• Use Delta Lake for model-ready datasets, feature storage and versioning
• Implement MLflow lifecycle including experiment tracking, model registry, versioning and deployment
• Build CI/CD pipelines for ML covering build, test, packaging and environment promotion
• Develop batch scoring pipelines for large-scale inference
• Develop real-time inference pipelines using APIs for online scoring
• Implement model monitoring including drift detection and performance tracking
• Define standards for reproducibility, lineage, auditability and governance of ML lifecycle
• Collaborate with data science and business teams for model validation and adoption

Mandatory Technical Skills

• Programming and Data
• Python, PySpark, SQL, Spark, Scala
• ML Libraries
• numpy, pandas, scikit-learn, tensorflow, pytorch, xgboost, lightgbm
• Databricks and Platform
• Databricks notebooks, jobs, workflows, Delta Lake
• MLOps
• MLflow tracking, MLflow model registry, MLflow deployment, CI/CD pipelines, batch inference, real-time inference