EY EY - GDS Consulting AI DATA -ML Ops Manager 

India, Telangana, Hyderabad 

820034541

Key Responsibilities

• Develop, deploy, and monitor machine learning models in production environments.
• Automate ML pipelines for model training, validation, and deployment .
• Optimize ML model performance, scalability, and cost efficiency.
• Implement CI/CD workflows for ML model versioning, testing, and deployment.
• Manage and optimize data processing workflows for structured and unstructured data.
• Design, build, and maintain scalable ML infrastructure on cloud platforms.
• Implement monitoring, logging, and alerting solutions for model performance tracking .
• Collaborate with data scientists, software engineers, and DevOps teams to integrate ML models into business applications.
• Ensure compliance with best practices for security, data privacy, and governance .
• Stay updated with the latest trends in MLOps, AI, and cloud technologies .

Technical Skills:

• Programming Languages: Proficiency in Python (3.x) and SQL .
• ML Frameworks & Libraries: Extensive knowledge of ML frameworks ( TensorFlow, PyTorch, Scikit-learn ), data structures, data modeling, and software architecture.
• Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases.
• Mathematics & Algorithms: Strong understanding of mathematics, statistics, and algorithms for machine learning applications.
• ML Modules & REST API: Experience in developing and integrating ML modules with RESTful APIs .
• Version Control: Hands-on experience with Git and best practices for version control.
• Model Deployment & Monitoring: Experience in deploying and monitoring ML models using:
• MLflow(for model tracking, versioning, and deployment)
• WhyLabs(for model monitoring and data drift detection)
• Kubeflow(for orchestrating ML workflows)
• Airflow(for managing ML pipelines)
• Docker & Kubernetes(for containerization and orchestration)
• Prometheus & Grafana(for logging and real-time monitoring)
• Data Processing: Ability to process and transform unstructured data into meaningful insights (e.g., auto-tagging images, text-to-speech conversions ).

Preferred Cloud & Infrastructure Skills:

• Experience with cloud platforms :Knowledge of AWS Lambda, AWS API Gateway, AWS Glue, Athena, S3 and Iceberg and Azure AI Studio for model hosting, GPU/TPU usage, and scalable infrastructure.
• Hands-on with Infrastructure as Code (Terraform, CloudFormation) for cloud automation.

Experience on CI/CD pipelines: Experience integrating ML models into continuous integration/continuous delivery workflows. We use Git based CI/CD methods mostly.

• Experience with feature stores (Feast, Tecton) for managing ML features.
• Knowledge of big data processing tools (Spark, Hadoop, Dask, Apache Beam) .

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