EY - GDS Consulting - AI and DATA -ML Ops- Senior
EY - GDS Consulting - AI and DATA -ML Ops- Senior
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Job Description
At EY youll 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 were 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).
EY Building a better working world
EY exists to build a better working world helping to create long-term value for clients people and society and build trust in the capital markets.
Enabled by data and technology diverse EY teams in over 150 countries provide trust through assurance and help clients grow transform and operate.
Working across assurance consulting law strategy tax and transactions EY teams ask better questions to find new answers for the complex issues facing our world today.
Employment Type
Full Time
