AI for DevOps initiative Steering Lead

BMW TechWorks India Private Limited; Chennai District; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Travel flexibility

Job summary

BMW TechWorks India Private Limited is seeking an experienced AI for DevOps Steering Lead to partner with cluster heads and lead developers to shape AI-enabled SDLC transformation. The role focuses on AI-powered practices, governance, and measurable outcomes in engineering productivity.

You will drive GenAI adoption, define an execution plan, and steer strategic AI initiatives across the organization to improve release velocity, reliability, and developer experience.

Qualifications

• 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.
• Strong 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.

Responsibilities

• Steer the AI for DevOps charter to accelerate software delivery, engineering productivity and platform reliability.
• Build and scale AI-assisted engineering practices including AI pair programming, AI code generation, AI-enabled testing.
• Introduce and operationalize AI Agents across the 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, and developer experience.
• Partner with Engineering Leaders and Architects to evaluate emerging AI technologies, tools, and partnerships.

Skills

Generative AI

LLMs

AI Agents

AI for DevOps

Cloud platforms

Kubernetes & Docker

IaC

Python/Java

Microservices

Vector databases

SDLC automation

AI tooling (Copilot etc)

Agile & SRE

Job description

AI Data Transformation Reporting and Analytics skills

Job Description

Role

AI for DevOps initiative Steering Lead

Location

• 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:
  • AI pair programming
  • AI code generation
  • AI-enabled testing
  • AI-driven CI/CD optimization
  • 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.
• Strong 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