AI for DevOps initiative Steering Lead (Tamil Nadu)

Role AI for DevOps initiative Steering Lead Location - Chennai / Bengaluru - Flexibility to travel for business trips Experience: - 15 to 20 years What awaits you/ Job Profile We are looking for an experienced AI Implementation execution steering person to work with Cluster Heads, Unit Heads, Titans and Lead Developers in developing & augmenting Agents/Skills/MCP in SDLC workflow. The role will steer the strategic AI for DevOps (AI-Assisted, AI Augmentation, AI Native SDLC). You will define an outcome-based execution plan to transform how applications are built, deployed, operated, and supported across the organization. - Steer the AI for DevOps charter to accelerate software delivery, engineering productivity and platform reliability - Build and scale AI-assisted engineering practices including: 1. AI pair programming 2. AI code generation 3. AI-enabled testing 4. AI-driven CI/CD optimization 5. AI-powered observability and incident management - Introduce and operationalize AI Agents across the Software Development Lifecycle (SDLC) phases (Plan, Code, Build, Test, Release, Deploy, Operate & Monitor) - Drive adoption of GenAI, LLMOps, Agentic AI, and automation platforms across development teams. - Enable team to AI Governance, Security, Compliance, Responsible AI Practices, and Model Lifecycle Management. - Create measurable outcomes around engineering productivity, release velocity, defect reduction, infrastructure optimization, and developer experience. - Partner with Engineering Leaders and Architects to evaluate emerging AI technologies, tools, frameworks, and strategic partnerships. What should you bring along - 15+ years of experience in Software Engineering, DevOps, Platform Engineering, Cloud Transformation, or Enterprise Architecture. - 5+ years of leadership experience driving enterprise-scale digital or AI transformation initiatives. - Strong experience of SDLC, CI/CD, cloud-native engineering, observability, and platform automation. - Robust experience in building engineering platforms, DevOps ecosystems, or developer productivity solutions. - Proven experience implementing AI/ML or Generative AI solutions in enterprise environments. - Experience working directly with executive leadership and driving cross-functional strategic programs. - Ability to balance strategy, architecture, execution, governance, and change management. - Excellent stakeholder management, communication, and leadership skills. - Entrepreneurial mindset with strong problem-solving and innovation capabilities. - Experience working with globally distributed engineering teams is preferred. Must have technical skill - Generative AI and Large Language Models (LLMs) - AI Agents / Agentic AI frameworks - AI-assisted software engineering tools - AI for DevOps frameworks - Cloud platforms: AWS, Azure, or Google Cloud - DevOps and CI/CD ecosystems - Kubernetes, Docker, and container orchestration, Infrastructure as Code (Terraform, Ansible, etc.) - Python, Java, or modern backend engineering experience - API architecture and microservices - Vector databases and RAG architectures - SDLC automation and developer platforms - GitHub Copilot, Cursor, OpenAI APIs, Claude, or equivalent AI engineering tools - Agile, DevSecOps, and Site Reliability Engineering (SRE) Good to have technical skills - Multi-agent orchestration frameworks - LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks - Knowledge graphs and enterprise search architectures - AI observability and model monitoring tools - Fine-tuning and model optimization techniques - AI-powered testing and QA automation - Event-driven and streaming architectures - Data engineering and real-time analytics platforms - Enterprise workflow automation platforms - Low-code / no-code AI platforms - Experience with enterprise copilots and productivity assistants - Experience building AI-native SaaS products - FinOps and CloudOps optimization - Responsible AI, compliance, and AI risk management frameworks - Experience with hybrid cloud and edge AI architectures - Exposure to enterprise collaboration platforms such as Microsoft Copilot ecosystem, Slack AI, or Google Workspace AI integrations .