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Tata Consultancy Services Hiring! Full Time Senior Machine Learning Engineer in - Ricebowl

Senior Machine Learning Engineer

Undisclosed

Singapore

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Working Location

  • Singapore Singapore

Job Description

Responsibilities

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 *************


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

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