DevOps & Cloud Engineering AI Enabled

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.