Role: DevOps Consultant / Architect
Industry Type: IT Services & Consulting
Department: Engineering - Software & QA
Employment Type: Full Time, Permanent
Role Category: DevOps
UG: Any Graduate
AI - DevOps Architect Experience: 1218 Years Location: Bangalore
Role Overview
We are looking for an experienced AI-DevOps Architect to lead the modernization of enterprise engineering platforms by combining DevOps best practices with AI-driven engineering capabilities. The ideal candidate will drive CI/CD transformation, establish agent-ready development environments, integrate AI into engineering workflows, and enable teams to adopt scalable, secure, and efficient software delivery practices.
Key Responsibilities
CI/CD Modernization Modernize and migrate legacy Jenkins pipelines to GitHub Actions. Design and implement reusable workflows, templates, and engineering standards to reduce platform fragmentation. Enhance observability, monitoring, and traceability across software delivery pipelines. Standardize CI/CD practices across engineering teams and business units. AI-Assisted Engineering Leverage AI technologies to accelerate code migrations, code analysis, testing, and documentation generation. Build secure, repeatable AI-driven workflows with strong validation and governance practices. Identify and implement high-impact AI use cases that improve engineering productivity. Agent-Ready Repositories Define repository standards, including metadata, documentation, instructions, templates, and governance controls. Ensure repositories are structured to support AI-powered tools while maintaining security and compliance. Establish best practices for repository organization and developer experience. AI Agent Workflows & Governance Integrate AI capabilities across GitHub, CI/CD platforms, Jira, and related engineering ecosystems. Define guardrails for access control, security reviews, code validation, approvals, and compliance. Implement human-in-the-loop validation mechanisms to ensure safe and reliable AI adoption. Platform Enablement & Adoption Drive adoption of standardized DevOps and AI engineering practices across teams. Develop reusable starter kits, golden paths, documentation, and onboarding frameworks. Gather feedback from engineering teams and continuously improve platform capabilities and developer experience. Required Experience & Skills DevOps & Platform Engineering Strong hands-on experience with GitHub Actions and Jenkins. Proven expertise in designing reusable workflows, developer platforms, and automation frameworks. Strong programming skills in Python, Bash, TypeScript, or Go. Experience with Docker, Kubernetes, and cloud-native application delivery. Deep understanding of GitHub ecosystem components, including Pull Requests, CODEOWNERS, branch protection rules, and repository governance. Experience leading enterprise-wide CI/CD transformation initiatives. AI & Agentic Engineering Hands-on experience with AI-powered development tools such as GitHub Copilot, Claude, Cursor, or similar platforms. Practical experience applying AI to software engineering activities including code analysis, testing, migration, and documentation. Expertise in managing repository-level AI context, prompts, instructions, and knowledge assets. Strong understanding of AI risks, including hallucinations, security vulnerabilities, data leakage, and over-automation. Experience implementing human-in-the-loop validation processes and responsible AI practices. Cloud & Automation Experience with cloud platforms such as Azure, AWS, or GCP. Knowledge of Infrastructure as Code (Terraform, CloudFormation, or equivalent). Familiarity with DevSecOps practices and security automation. Leadership Expectations End-to-End Ownership: Drive platform modernization initiatives from strategy through execution. Execution Focus: Deliver practical tooling, automation, and measurable outcomes. Innovation Mindset: Translate emerging AI capabilities into scalable engineering solutions. Strategic Thinking: Balance enterprise standardization with project-specific needs. Influential Leadership: Lead by example, mentor teams, and drive adoption through tangible results. Continuous Improvement: Foster a culture of automation, innovation, and engineering excellence. Preferred Qualifications Experience leading large-scale DevOps transformation programs. Exposure to AI-powered software development lifecycle (SDLC) frameworks. Experience building Internal Developer Platforms (IDP) and developer self-service capabilities. Relevant certifications in Cloud, Kubernetes, DevOps, or AI technologies are preferred.