ML OPS - Senior Engineer

Job Description

Required Skills & Experience

. 6-8 years of overall experience, including 3+ years in MLOps or DevOps for ML systems.

. Hands-on expertise with AWS SageMaker - model training, endpoint deployment, pipelines, and model registry.

. Strong experience with AWS services: S3, Lambda, ECR, ECS/EKS, CloudWatch, IAM, CloudFormation, and AWS CDK.

. Proven experience in AWS CDK (Cloud Development Kit) using TypeScript for infrastructure automation and environment provisioning.

. Expertise in building and managing CI/CD pipelines for ML workflows (AWS CodePipeline, Jenkins, or GitHub Actions).

. Proficiency in containerization technologies - Docker and Kubernetes.

. Strong scripting and automation skills using Python, TypeScript, and Bash.

. Experience with monitoring, logging, and drift detection for production ML models.

. Deep understanding of ML lifecycle management - model packaging, versioning, testing, and deployment.

. Familiarity with data pipelines and collaboration with Data Engineering teams for feature ingestion.

. Working knowledge of security, compliance, and governance practices in the banking/financial domain.

. Excellent communication, leadership, and stakeholder management skills across onsite-offshore teams

Mandatory Competencies