About the Role

The organization is moving beyond AI-assisted engineering towards a full AI-Driven Development Lifecycle (AIDLC) — an AI-native way of working where AI agents drive the bulk of planning, code, and test generation under human oversight, across the Inception, Construction, and Operations phases. We are looking for a senior architect to own the vision and hands-on execution of this shift across the Software Engineering and testing landscape. This person is the transformation change driver who defines AI-first engineering and quality practices, proves them on real workloads, and scales them into standard operating practice.

Responsibilities

- Own the vision and hands-on execution of the shift to a full AI-Driven Development Lifecycle (AIDLC) across the Software Engineering and testing landscape.
- Act as the transformation change driver who defines AI-first engineering and quality practices.
- Prove AI-first engineering and quality practices on real workloads.
- Scale AI-first engineering and quality practices into standard operating practice.
- Architect end-to-end AI-DLC workflows: Define AI-enabled delivery across inception, construction, and operations, with human approval gates, traceable artifacts, and explicit accountability for architectural decisions.
- Demonstrate hands-on GitHub Copilot expertise: Configure repository instructions, reusable prompt files, and custom agents; apply Copilot to development, testing, code review, and pull-request workflows.
- Implement spec-driven development using AWS Kiro: Create and maintain specifications, steering files,



and agent hooks to enforce architectural standards and automate code, documentation, and test-generation activities.
- Demonstrate hands-on Claude Code expertise: Use codebase exploration, Plan Mode, multi-file implementation, debugging, and refactoring; configure CLAUDE.md, skills, hooks, and subagents for repeatable engineering workflows.
- Establish context-engineering standards: Structure repository knowledge, coding conventions, architectural constraints, and reusable instructions so agents receive relevant context without unnecessary context-window consumption.
- Integrate agents with enterprise engineering tools: Design controlled Model Context Protocol (MCP) integrations with approved tools and APIs, and incorporate agent-driven tasks into existing development and CI workflows.
- End-to-end engineering orchestration: Architect workflows spanning requirement analysis, codebase exploration, implementation, testing, and pull-request creation, combining deterministic controls with agent-driven planning where appropriate.
- Codebase intelligence and context management: Architect context-gathering services that help agents discover relevant files, understand existing implementation patterns,



and access project knowledge while managing context-window limits.
- Agent evaluation and quality gates: Build evaluation harnesses using representative engineering tasks, automated tests, build results, and independent verification to assess agent changes before release.
- Platform observability and optimization: Implement execution traces, tool-call visibility, failure diagnostics, and performance monitoring to investigate agent behavior and optimize production workflows.
- AI Red Teaming & Security Testing: Prompt injection testing, jailbreak testing, data leakage testing, adversarial testing, and AI-to-AI testing.
- Test Automation & AI QA Tooling: TestSprite and Playwright for AI-assisted and end-to-end test automation.

Required Skills

- Practical experience with AI-native SDLC methods and agentic coding workflows (e.g., GitHub Copilot, Cursor, AWS AI-DLC / Kiro / Amazon Q Developer style adaptive workflows, or equivalent).
- Must have successfully implemented Legacy Modernization and Brownfield Enhancements using AIDLC framework in GitHub Copilot, Cursor, AWS Kiro for Java, .NET, Mainframe programming languages.
- Strong grounding in agent architectures — tool/function calling, orchestration, memory, retries, and guardrails.
- Building Agent Harness using GitHub Copilot, AWS Kiro, Claude Code.
- Experience building enterprise Agentic AI platforms for software engineering AIDLC.
- Hands-on experience with Java Springboot, Python, React technologies and Azure and AWS cloud for application development and testing tools.

📌 Senior Architect AIDLC (Pune)
🏢 Last Working Day
📍 Pune