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 setting 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.

Job Snapshot

Updated Date

17-08-2026

Job ID

Harb722

Department

Resource Management Group

Location

Baner, Pune, Maharashtra, India

Experience

6 - 8 Years

Employee Type

Consultant

📌 DevOps & Cloud Engineering – AI Enabled Consultant (India)
🏢 Harbinger Group
📍 India