AWS DevOps Engineer (With deep expertise in MLOps)

About This Role

This role will design, deploy, and support a scalable SageMaker platform for Toyota Financial Services, accelerating the end-to-end machine learning lifecycle. The engineer will empower data scientists by enabling seamless model development, versioning, deployment, and monitoring in production.

Responsibilities

• Design, deploy, and maintain a robust AWS SageMaker platform to support the full ML lifecycle
• Collaborate closely with data scientists to productionize machine learning models, ensuring scalability, reliability, and performance
• Implement model versioning, lineage tracking, and governance to support reproducibility and auditability
• Build and maintain MLOps pipelines that automate continuous integration, continuous delivery (CI/CD), and continuous training (CT) of ML models
• Manage AWS infrastructure including EC2, ECS Fargate, ALB, S3, DynamoDB, OpenSearch, and AWS Bedrock to support AI/ML workloads
• Enforce enterprise security best practices using IAM, Guardrails, and AWS security services
• Configure and manage Single Sign-On (SSO) integration with Okta and Mulesoft proxy for secure platform access
• Automate infrastructure provisioning and management using Infrastructure as Code (IaC) tools such as Terraform and OpenTofu
• Monitor deployed models and infrastructure for performance, drift, and anomalies; implement alerting and remediation workflows
• Support containerized microservices architecture with Python-based services, establishing CI/CD pipelines for rapid deployment
• Stay current with AWS services, MLOps frameworks, AI ecosystem trends, and DevOps best practices

Requirements

• 5+ years of hands-on experience in AWS cloud infrastructure and DevOps engineering with a strong focus on MLOps
• Expertise in AWS services: SageMaker (including SageMaker Pipelines, Model Registry), ECS Fargate, EC2, ALB, S3, DynamoDB, OpenSearch, AWS Bedrock
• Proven experience in productionizing ML models, managing model versioning, lineage, and lifecycle
• Strong skills in Infrastructure as Code using Terraform and OpenTofu
• Experience designing and implementing CI/CD pipelines for ML workflows and Python microservices
• Deep understanding of AWS security best practices, IAM policies, Guardrails, and enterprise security frameworks
• Experience integrating Single Sign-On (SSO) solutions using Okta and Mulesoft proxy
• Familiarity with ML lifecycle management tools and frameworks
• Strong scripting and automation skills
• Excellent problem-solving, collaboration, and communication skills

Qualifications

• 5+ years of hands-on experience in AWS cloud infrastructure and DevOps engineering with a strong focus on MLOps.

Nice to Have

• AWS certifications (e.g., AWS Certified DevOps Engineer, AWS Certified Machine Learning Specialty)
• Experience with container orchestration, microservices, and serverless architectures
• Knowledge of monitoring, logging, and alerting tools for ML models and cloud infrastructure
• Familiarity with open-source MLOps tools (e.g., MLflow, Kubeflow)

Skills

* Required skills

Benefits