EY - GDS Consulting - AI and DATA -ML Ops- Senior

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

JD for ML Ops

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.

Mandatory Skills

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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