Machine Learning Ops Engineer

inovalon

Gurugram 4 Years Exp Posted 49d ago

Key responsibilities

Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code.
Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring.
Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake.
Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services.
Automate environment provisioning, configuration, and dependency management across dev, test, and production.
Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA).
Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services.
Contribute to best practices, standards, and documentation for ML platform and operations across the organization.

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
4+ years of experience in software engineering, data engineering, or ML engineering with at least 2+ years focused on MLOps or ML platform work.
Strong proficiency with Python and experience integrating ML libraries or frameworks (e.g., scikit-learn, TensorFlow, PyTorch) into production workflows.
Hands-on expertise with AWS services relevant to MLOps: SageMaker, Bedrock, IAM, CloudWatch, ECR, ECS/EKS or Lambda, S3, Step Functions, and Glue.
Experience with Snowflake (including Snowflake Cortex), SQL, and building secure, performant data pipelines into and out of Snowflake.
Proficiency with CI/CD tools (e.g., GitHub Actions, GitLab CI, CodePipeline) and infrastructure-as-code (e.g., Terraform, CloudFormation, CDK).
Familiarity with containerization and orchestration (Docker, Kubernetes) and event streaming tools (e.g., Kafka) is a plus.
Knowledge of software engineering best practices, including testing, code reviews, version control, and design for reliability and scalability.
Experience in regulated domains or with healthcare data standards and regulations is a plus (e.g., HIPAA, FHIR, HL7).

Excellent problem-solving and analytical skills with a focus on reliability and automation.
Strong communication and collaboration abilities, including working cross-functionally with engineering, data science, and product teams.
Ability to work independently in a fast-paced environment
Competitive salary and benefits package.
Opportunity to work on impactful ML platforms that improve healthcare outcomes.
- Collaborative, innovative environment with professional development and growth opportunities.