Leads the integration of AI throughout the software development lifecycle, including requirements, design, coding, testing, code review, defect detection, and deployment. Designs reusable AI-driven workflows, prompt frameworks, and development accelerators to standardize engineering practices, improve code quality and test coverage, reduce manual effort, and increase productivity. Drives adoption and consistency across engineering teams through context-aware development and quality-focused processes.
This role focuses on embedding AI across the software development lifecycle (SDLC) to improve developer productivity, enhance code quality, and reduce manual effort. The expectation is not limited to AI-assisted coding, but to systematically integrate AI across planning, development, testing, and release workflows, while establishing consistent and reusable engineering practices.
Key Responsibilities
- Leverage AI-assisted tools (e.g., GitHub Copilot, code agents) as an integral part of engineering workflows, not as isolated accelerators.
- Apply AI across the end-to-end SDLC (planning → coding → testing → deployment) to improve speed, quality, and consistency of delivery.
- Drive adoption of AI beyond development into areas such as:
- Requirement understanding and design support
- Test generation and validation
- Code review and defect detection
- Design and implement AI-driven workflows that reduce manual steps, minimize rework, and improve overall engineering efficiency.
- Establish and scale standardized, reusable patterns, including:
- Prompt frameworks
- Coding and design accelerators
- AI-enabled development workflows
- Ensure consistent, high-quality code output across teams, independent of individual developer variability, improving maintainability and adherence to enterprise standards.
- Use AI to enhance quality engineering practices, including:
- Increasing test coverage
- Enabling early defect identification
- Supporting structured and consistent code reviews
- Enable context-aware development, leveraging codebase, documentation, and work item context (tickets, user stories) to drive more accurate and aligned implementations.
- Reduce context gaps and inefficiencies, improving developer decision-making, productivity, and alignment to business and architectural intent.
Requirements
What This Role Specifically Emphasizes
- AI applied to engineering workflows, not just model building
- End-to-end SDLC integration, not isolated tool usage
- Standardization and scale, not individual productivity hacks
- Quality and consistency, not just speed
Primary Skills
1. AI-Driven SDLC Thinking
2. Engineering Workflow Optimization
3. Standardization & Scale Mindset
Secondary Skills (Important Differentiators)
1. Quality-First Engineering Approach
2. Context-Aware Problem Solving
3. Adoption & Influence Across Teams
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To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
