DevOps Engineer — AWS / ML Infrastructure

We are seeking a skilled and proactive DevOps Engineer to join our team. This role is focused on developing and managing scalable infrastructure and deployment workflows in AWS to support data-driven and machine learning applications. 

You will play a key role in building cloud-native systems with a strong emphasis on infrastructure as code, containerization, and CI/CD pipelines. 

A solid understanding of AWS services and Python is essential, particularly for authoring infrastructure using AWS CDK. Experience with SageMaker and knowledge of ML systems is a strong advantage.

Qualifications:

• 3–5 years of experience in DevOps, cloud infrastructure, or SRE roles.
• Proficient in AWS services, especially CDK, Lambda, EC2, S3, SageMaker, and CloudWatch.
• Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation.
• Strong experience with Python for scripting and infrastructure automation.
• Hands-on experience with containerization (Docker).
• Experience building and maintaining CI/CD pipelines.

Preferred Qualifications:

• AWS Certifications (e.g., DevOps Engineer, Solutions Architect, or Machine Learning Specialty).
• Background in software engineering or ML/AI infrastructure is a plus.

Key Responsibilities:

Infrastructure Development & Automation:

• Design, provision, and manage AWS infrastructure using AWS CDK and CloudFormation.
• Develop secure, scalable, and cost-efficient infrastructure to support machine learning and analytics workloads.
• Implement and manage cloud-native services such as EC2, ECS, Lambda, S3, RDS, SageMaker, and Bedrock.
• Ensure best practices for security, compliance, and disaster recovery are followed.

CI/CD & Deployment Automation:

• Design and maintain CI/CD pipelines for application and model deployment using tools like CodePipeline, CodeBuild, GitHub Actions, or similar.
• Automate testing, deployment, and rollback procedures to support continuous integration and delivery.

Containerization & Orchestration:

• Build and manage Docker containers for microservices and ML applications.
• Support deployment on ECS or Lambda with container-based runtimes.
• Implement image build, versioning, and artifact management workflows.

Machine Learning & Model Operations Support:

• Collaborate with ML engineers to deploy, monitor, and maintain models in SageMaker.
• Integrate infrastructure for pre-processing, inference, and retraining pipelines.
• Support model performance monitoring, logging, and metrics collection.

Monitoring, Observability & Logging:

• Set up monitoring and alerting using CloudWatch, DataDog, and other observability tools.
• Troubleshoot and resolve infrastructure, deployment, and performance issues proactively.

Collaboration & Documentation:

• Work closely with software, ML, and data teams to support DevOps best practices across the ML lifecycle.
• Maintain clear documentation for infrastructure, deployments, and operations processes.
• Participate in code reviews and architectural discussions.