Devops Engineer

Job Role: DevOps Engineer- AI/ML Infrastructure

Years of experience: 7+

Location: Remote

Role Summary

We are seeking a DevOps Engineer, AI/ML Infrastructure to provide technical leadership for the cloud platforms, deployment systems, and operational foundations that power enterprise-scale generative AI applications.

This role will define and evolve the infrastructure architecture for AI/ML platforms running across AWS, Kubernetes, serverless, and containerized environments. The engineer will lead platform standards for reliability, scalability, observability, CI/CD, security, and developer enablement, while partnering closely with software engineering, AI engineering, security, and operations teams.

The ideal candidate combines deep hands-on cloud engineering experience with staff-level technical influence. They are comfortable designing infrastructure patterns, writing infrastructure-as-code, improving delivery pipelines, mentoring engineers, and making architectural decisions that raise the operational maturity of AI platforms across multiple teams.

Key Responsibilities

• Define and drive the technical strategy for AI/ML platform infrastructure supporting generative AI applications, LLM integrations, model routing, and enterprise AI services.
• Architect, build, and operate scalable cloud platforms using AWS services such as EKS, ECS Fargate, Lambda, DynamoDB, S3, OpenSearch, Secrets Manager, CloudWatch, ALB, and MWAA. Establish reusable infrastructure patterns using CloudFormation, Helm, and Terraform to support reliable multi-environment and multi-region deployments
• Lead CI/CD architecture using GitHub Actions, reusable workflows, OIDC-based AWS authentication, automated quality gates, deployment promotion, and environment approvals.
• Design and improve observability across AI platforms, including CloudWatch dashboards, logs, alarms, Prometheus/Grafana, OpenSearch, Langfuse, and LLM-specific operational metrics.
• Build platform capabilities for GenAI workloads, including model availability monitoring.
• Partner with software engineering teams to improve deployment reliability, rollback strategies, health checks, autoscaling, load testing, and runtime performance.
• Define and enforce security and compliance practices for infrastructure, including IAM permission boundaries, Secrets Manager usage, secret scanning, audit logging, tagging standards, and change-management controls.
• Provide technical leadership for cost optimization, capacity planning, environment standardization, and operational resilience across development, test, production, and sandbox environments.
• Mentor engineers, review architecture and infrastructure designs, and influence platform engineering practices across teams.
• Troubleshoot complex production issues across cloud infrastructure, networking, containers, serverless workloads, CI/CD systems, and observability platforms.
• Translate enterprise requirements for security, compliance, reliability, and governance into pragmatic engineering standards and automation.

Basic Qualifications

• Bachelors degree in Computer Science, Engineering, Information Technology, or a related technical field, or equivalent practical experience.
• 7+ years of experience in DevOps, platform engineering, cloud infrastructure, site reliability engineering, or software engineering roles
• Strong hands-on experience with AWS/Azure/GCP infrastructure and services, including container, serverless, networking, storage, observability, and security services.
• Experience designing and operating production systems on Kubernetes, ECS/Fargate, or comparable container orchestration platforms
• Proficiency with infrastructure-as-code, especially CloudFormation, Terraform, Helm, or similar tooling.
• Strong CI/CD experience with GitHub Actions or similar platforms, including reusable workflows, automated testing, deployment gates, and cloud authentication.
• Experience building and operating observability solutions using CloudWatch, Prometheus/Grafana, OpenSearch, or similar tools.
• Strong understanding of cloud security practices, IAM, secrets management, least-privilege access, audit logging, and compliance requirements.
• Experience supporting distributed systems, microservices, APIs, asynchronous workloads, and multi-environment deployments.
• Demonstrated ability to lead technical design, mentor engineers, and influence engineering practices across teams.

Technical Environment

This role works across a modern AI platform ecosystem including:

• Cloud: AWS EKS, ECS Fargate, Lambda, DynamoDB, S3, OpenSearch, CloudWatch, Secrets Manager, ALB, VPC, IAM
• Infrastructure-as-Code: CloudFormation, Helm, Terraform
• CI/CD: GitHub Actions, reusable workflows, OIDC federation, environment approvals, automated release promotion
• AI/ML Platform: AWS Bedrock, Azure OpenAI, LiteLLM, Langfuse
• Observability: CloudWatch dashboards and alarms, Prometheus, Grafana, OpenSearch, Langfuse, custom metrics
• Security & Governance: IAM permission boundaries, secret scanning, audit logging, tagging compliance, change-management automation
• Engineering Practices: Docker, Python, pre-commit, automated testing, load testing, code quality gates, monorepo service standards