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Job Description: EY GDS – Data and Analytics (D&A) – Senior- MLops

Role Overview: We are seeking a highly skilled and experienced Staff Data Scientist with a minimum of 1 - 3 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have 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.

Responsibilities:Job Description: EY GDS – Data and Analytics (D&A) – Senior– ML Ops

Role Overview: We are seeking a highly skilled and experienced Staff Data Scientist with a minimum of 1 - 3 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have 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.

Responsibilities:Your technical 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 (Tensor Flow, Py Torch, 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)

• Why Labs (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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