DevOps Engineer – ML/AI (DevSecOps)

Job Description

• Design, implement, and manage secure, scalable AWS cloud infrastructure for production applications and AI/ML workloads.

• Build and maintain secure CI/CD pipelines using GitHub Actions, Jenkins, and GitLab CI.

• Implement Infrastructure as Code (IaC) using Terraform, AWS CloudFormation, and Ansible.

• Deploy, manage, and optimize containerized applications using Docker, Kubernetes (EKS), and Amazon ECS.

• Develop automation scripts using Python, Bash, and Go to streamline cloud operations and deployment processes.

• Manage production ML/AI inference infrastructure, including GPU-backed workloads, model serving, and scalable inference endpoints.

• Work with AWS SageMaker, Amazon Bedrock, vector databases, and AI agent infrastructure.

• Implement cloud security best practices, including IAM, encryption, secrets management, vulnerability scanning, and Zero Trust architecture.

• Integrate DevSecOps practices and security testing throughout the Secure Software Development Life Cycle (SSDLC).

• Conduct vulnerability assessments, container image scanning, and infrastructure security reviews using Aqua Security, Prisma Cloud, and Snyk.

• Monitor system performance, availability, and security using ELK Stack, Prometheus, and Grafana.

• Support production incident response, root cause analysis, disaster recovery, and reliability engineering.

• Ensure compliance with SOC 2, ISO 27001, HIPAA, OWASP LLM Top 10, and NIST AI Risk Management Framework requirements.

• Implement security controls for AI systems, LLM applications, agentic AI workflows, and model-serving environments.

• Optimize cloud resource utilization and costs using FinOps principles.

• Collaborate with DevOps, SRE, MLOps, AI/ML, and security teams to deliver reliable and secure production infrastructure.