Job Description
Role & responsibilities
Job Description
Consultant: ATS DevOps & Cloud Engineering – AI Enabled
Experience - 6–8 years
Location- Pune
Mode- Freelancer
Role Overview
We are looking for a hands-on DevOps / Cloud Engineer responsible for automating software delivery, managing cloud infrastructure, improving application reliability, and implementing secure, scalable cloud-native environments across AWS, Azure and GCP.
The candidate should have strong hands-on expertise in CI/CD, Infrastructure as Code, containers, Kubernetes, cloud services, monitoring and DevSecOps, along with practical experience using AI/GenAI tools to improve DevOps automation, troubleshooting and engineering productivity.
Required Skills
Must Have
- 6–8 years of hands-on DevOps / Cloud experience
- Strong experience in one major cloud – AWS / Azure / GCP
- Working knowledge of at least one additional cloud
- Strong CI/CD experience
- Terraform / Infrastructure as Code
- Docker
- Kubernetes
- Git / Git-based workflows
- Linux
- Scripting – Python / Bash / PowerShell
- Monitoring, logging and troubleshooting
- Strong understanding of networking fundamentals
- Security fundamentals / DevSecOps
AI – Must Have
- Practical experience using AI coding/productivity tools
- Understanding of AI-assisted DevOps automation
- Ability to use AI for troubleshooting, scripting and documentation while validating outputs
Good to Have
- AWS + Azure + GCP exposure
- Helm
- Argo CD / GitOps
- Prometheus / Grafana / OpenTelemetry
- Datadog
- Backstage / IDP
- Ansible
- Vault / secrets management
- FinOps / cloud cost optimization
- AI agents / Agentic AI
- MCP
- MLOps / AI workload deployment
Key Responsibilities
DevOps & CI/CD
- Design, implement and maintain CI/CD pipelines using tools such as GitHub Actions, Azure DevOps, Jenkins or GitLab CI.
- Automate build, test, deployment and release processes.
- Implement branching, release and deployment strategies including blue-green, canary and rolling deployments.
- Improve deployment reliability, speed and repeatability.
Multi-Cloud Engineering
- Work hands-on with AWS, Azure and/or GCP cloud environments.
- Provision and manage cloud infrastructure including compute, networking, storage, IAM and managed services.
- Understand equivalent services across cloud platforms and recommend appropriate solutions based on workload requirements.
- Support cloud migration, modernization and cost optimization initiatives.
Important: one cloud experience is mandatory + working exposure to other cloud rather than deep expertise in all three.
Infrastructure as Code & Automation
- Develop reusable infrastructure using Terraform or equivalent IaC tools.
- Automate infrastructure provisioning, configuration and environment management.
- Implement infrastructure standards, reusable modules and automated policy controls.
- Maintain version-controlled infrastructure and configuration.
Containers & Kubernetes
- Build and manage Docker containers and containerized applications.
- Work with Kubernetes / EKS / AKS / GKE environments.
- Support deployments, scaling, configuration, secrets and troubleshooting.
- Exposure to Helm, GitOps and tools such as Argo CD is preferred.
DevSecOps
- Integrate security into CI/CD and infrastructure workflows.
- Implement:
oSAST
oDAST
odependency scanning
ocontainer/image scanning
osecrets detection
- Follow secure cloud/IAM practices and support vulnerability remediation.
Observability & SRE
- Implement application and infrastructure monitoring, logging and alerting.
- Work with tools such as CloudWatch, Azure Monitor, GCP Operations Suite, Prometheus, Grafana, Datadog or OpenTelemetry.
- Support incident investigation, root-cause analysis and performance troubleshooting.
- Contribute to reliability, availability and operational excellence.
AI / GenAI Expectations
AI-Assisted DevOps
- Use AI tools such as GitHub Copilot, Amazon Q, Gemini, Claude or ChatGPT to improve:
oInfrastructure scripting
oYAML/pipeline development
oTerraform generation/review
otroubleshooting
olog analysis
odocumentation
otest automation
- Demonstrate ability to validate and secure AI-generated code/configuration rather than blindly accepting it.
AI is increasingly being incorporated into DevOps workflows for planning, coding, code review, security and operational troubleshooting.
AI / Agentic DevOps – Good to Have
- Exposure to AI agents / Agentic AI for IT operations.
- Understanding of how AI can support:
oincident triage
oroot-cause analysis
oautomated remediation
odeployment analysis
ocloud resource optimization
oinfrastructure monitoring
- Exposure to MCP (Model Context Protocol) or AI-agent integration with DevOps tools is a plus.
AWS, for example, now provides AI-powered DevOps capabilities that can query infrastructure, metrics, alarms, deployments and incident patterns using natural language.
Platform Engineering – Preferred
For stronger ATS-level candidates:
- Understanding of Platform Engineering / Internal Developer Platforms (IDP).
- Build reusable "Golden Paths" for development and deployment.
- Automate environment provisioning and developer self-service.
- Exposure to Backstage or similar developer portals is a plus.
Platform engineering is increasingly being positioned as the layer that provides reusable, self-service infrastructure, CI/CD and deployment capabilities to development teams.