DevOps Engineer

DevOps Manager / Lead DevOps EngineerJob Summary

We are seeking an experienced DevOps Manager / Lead DevOps Engineer with 10+ years of IT experience, including at least 8 years in DevOps, cloud infrastructure, automation, or Site Reliability Engineering (SRE).

In this hands-on leadership role, you will design, implement, and optimize secure, scalable, and cost-efficient cloud platforms across AWS, Azure, GCP, and hybrid environments. You will lead initiatives in CI/CD, Kubernetes, Infrastructure as Code (IaC), DevSecOps, and AI platform operations.

The successful candidate will have experience leading enterprise DevOps transformations and supporting banking, data analytics, or AI-driven systems.

Key ResponsibilitiesDevOps and CI/CD

• Design, implement, and manage end-to-end CI/CD pipelines using GitLab CI/CD, Jenkins, AWS CodePipeline, and GitHub Actions.
• Build multi-stage YAML pipelines covering builds, tests, security scans, and deployments.
• Establish branching strategies, release controls, rollback plans, and approval workflows.
• Improve pipeline performance through parallel execution, caching, and dynamic runners.

Cloud and Infrastructure

• Design and manage secure, highly available cloud infrastructure, primarily on AWS, with experience in Azure, GCP, or hybrid environments.
• Lead IaC initiatives using Terraform, CloudFormation, Ansible, Chef, or Puppet.
• Manage services such as EKS, AKS, GKE, EC2, RDS, S3, IAM, VPC, Lambda, SNS, SQS, EMR, and WAF.
• Optimize cloud costs through resource monitoring, right-sizing, and billing controls.

Kubernetes and Containers

• Lead containerization and orchestration using Docker, Podman, Kubernetes, and managed services such as EKS, AKS, and GKE.
• Build and maintain secure container base images and implement image scanning and lifecycle management.
• Improve cluster performance, scaling, and reliability across staging, UAT, and production environments.

Monitoring, Observability, and Reliability

• Implement monitoring, logging, alerting, and dashboards using AWS CloudWatch and related services.
• Improve observability, incident response, and root-cause analysis.
• Apply SRE practices to strengthen reliability, performance, and operational excellence.

AI Platforms and MLOps

• Design and operate AI infrastructure on Kubernetes, including Amazon EKS.
• Build and support MLOps pipelines and AI services using tools such as Amazon Bedrock, LangChain, LangGraph, MCP, and FastAPI.
• Integrate vector databases such as ChromaDB, Pinecone, and OpenSearch for semantic search and analytics.
• Automate model deployment, retraining workflows, and AI service scaling.

Leadership and Collaboration

• Lead, mentor, and support DevOps and cloud engineering teams.
• Work closely with data engineering, QA, AI, and business teams.
• Promote documentation, knowledge sharing, and consistent DevOps practices.
• Support AWS Marketplace deployments and enterprise customer onboarding.

Required Skills and Qualifications

• Cloud: Expert-level AWS experience; familiarity with Azure, GCP, and hybrid environments.
• CI/CD: GitLab CI/CD, Jenkins, AWS CodePipeline, or GitHub Actions.
• Infrastructure as Code: Terraform, CloudFormation, Ansible, Chef, or Puppet.
• Containers: Docker, Podman, Kubernetes, EKS, AKS, or GKE.
• Programming and scripting: Python, Bash, PowerShell, or Groovy.
• Databases: PostgreSQL, MySQL, MongoDB, SQL Server, or ChromaDB.
• AI/ML tools: Amazon Bedrock, OpenAI, Anthropic, LangChain, LangGraph, or Pydantic AI.
• Monitoring and security: AWS CloudWatch, SonarQube, and DevSecOps tools and practices.
• Experience supporting banking, financial services, or other regulated environments.

Experience

• 10+ years of experience in IT infrastructure, including 8+ years in DevOps, cloud, or automation.
• Experience leading DevOps or cloud engineering teams.
• A track record of delivering enterprise cloud or DevOps initiatives.
• Experience with banking clients, large financial institutions, or similarly complex organizations.
• Experience supporting multi-cloud, AI-driven, or data-intensive platforms.