EY - GDS Consulting - AI And DATA -ML Ops - Manager

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Job Description: Manager ML Ops

Role Overview:

We are seeking an accomplished and visionary Manager Data Scientist with minimum 8 Years of experience in Data Science and Machine learning, preferable experience around NLP, Generative AI, LLMs, MLOps, Optimization techniques and AI solution Architecture to lead our AI team and drive the strategic direction of AI initiatives. In this role you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise and leadership skills. The ideal candidate should have a proven track record in AI leadership, a deep understanding of AI technologies, and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Minimum 8 Years of experience in Data Science and Machine learning. Excellent leadership skills with at least 2-3 years of people management OR technical architecture experience.

Responsibilities:

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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