MLOps Engineer

Job Description We are seeking an experienced MLOps / ML Engineer with strong AWS expertise to design, deploy, and manage scalable ML solutions. The ideal candidate will have hands-on experience in building CI/CD pipelines, automating ML workflows, and deploying production-grade models in cloud environments. Key Responsibilities • Design, implement, and maintain MLOps pipelines for model training, testing, and deployment. • Build and manage CI/CD automation for ML projects using tools like GitHub Actions, GitLab, Jenkins, or AWS CodePipeline. • Work with AWS services such as SageMaker, Lambda, ECR, ECS/EKS, S3, and Step Functions. • Implement containerized deployments using Docker and Kubernetes. • Develop and orchestrate ML workflows using SageMaker Pipelines, Kubeflow, or similar tools. • Collaborate with data engineers and data scientists to support model lifecycle management and data workflows. Required Qualifications • 5+ years of experience in MLOps or ML Engineering. • Strong expertise in AWS cloud ecosystem (SageMaker, Lambda, ECR, ECS/EKS, S3, Step Functions). • Proficiency with CI/CD automation and version control. • Hands-on experience with containerization (Docker, Kubernetes). • Solid understanding of ML pipeline orchestration. • Bachelor's or Master’s degree in Computer Science, Data Science, or a related field. Preferred Qualifications • Prior experience in the financial services industry. • Experience implementing enterprise model lifecycle management. • Familiarity with data engineering tools (Glue, EMR, Spark, PySpark). • Working knowledge of Infrastructure as Code (Terraform, AWS CloudFormation).