Position Overview

Sparient Global LLP is seeking a hands-on AI-Enabled SDLC Consultant to transform how our engineering teams analyze requirements, design solutions, develop, test, review, deploy, document, and maintain software using Generative AI and AI-assisted development tools.

This is not a generic AI training role. The consultant must combine strong software engineering experience with practical AI implementation, working directly with developers, architects, QA, DevOps, project managers, and technical leadership on real Sparient projects.

Core Responsibilities

AI-Assisted SDLC & Requirements

- Assess current SDLC practices and identify high-value AI opportunities.
- Use AI to analyze requirements/RFPs/SOWs; create user stories, acceptance criteria, specifications, task breakdowns, and project plans.
- Establish repeatable AI workflows from requirements through maintenance.

Architecture & Development

- Apply AI to solution architecture, technology evaluation, API/database design, documentation, risk analysis, legacy-code understanding, and modernization.
- Coach developers in AI-assisted coding, debugging, refactoring, optimization, code generation, and framework migration.
- Demonstrate practical use of GitHub Copilot, Claude Code, Cursor, ChatGPT, Microsoft Copilot, or comparable tools.

Testing, Quality & Security

- Implement AI-assisted unit/integration/functional testing, test-data generation, regression testing, edge-case analysis, defect/root-cause analysis, and quality improvement.
- Establish AI-assisted code review, security analysis, dependency review, secure coding, and performance-analysis practices.
- Ensure AI-generated code receives appropriate human validation.

DevOps, Documentation & Agents

- Apply AI to CI/CD troubleshooting, build/deployment issues, log analysis, cloud operations, incidents, monitoring, and release documentation.




- Automate creation and maintenance of technical, API, architecture, user, and support documentation.
- Introduce AI coding agents, repository-aware agents, agentic workflows, and MCP-based workflows where appropriate, with human oversight.

Training & Coaching

- Deliver hands-on workshops and coaching for developers, architects, QA, DevOps, BAs, and project managers.
- Use real Sparient applications/codebases rather than training exclusively with generic examples.
- Establish internal AI champions and coach teams toward sustained adoption.

Key Deliverables

- AI/SDLC readiness and opportunity assessment.
- AI adoption roadmap and recommended toolset.
- Sparient AI-assisted development standards covering prompting, coding, testing, security, privacy, IP/confidentiality, and human review.
- Role-based hands-on training and workshops.
- Implementation of AI-assisted practices on at least one live project.
- Sparient AI SDLC Playbook with reusable prompts, workflows, patterns, and governance recommendations.
- AI-agent and SDLC automation roadmap.

Required Qualifications

- 8+ years professional software engineering experience; 3+ years practical Generative AI experience in software engineering.
- Demonstrated experience implementing AI-assisted development within real software teams.
- Strong SDLC, Agile/Scrum, architecture, code review, testing, DevOps, CI/CD, and cloud knowledge.
- Hands-on experience with one or more of GitHub Copilot, Claude Code, Cursor,



ChatGPT, Microsoft Copilot, Azure AI/Microsoft Foundry, AI coding agents, or comparable platforms.
- Robust understanding of LLMs, prompt engineering, AI-assisted coding, agentic workflows, RAG concepts, MCP, and AI governance.
- Experience with .NET/ASP.NET Core, C#, JavaScript/TypeScript, React/Angular, Python, REST APIs, SQL, Git/GitHub, Azure DevOps, and cloud platforms is desirable.
- Microsoft/Azure and Microsoft 365 experience is highly desirable.

Preferred Consulting Experience

- AI transformation or developer-productivity consulting; enterprise AI adoption; GitHub Copilot implementation; AI coding-agent implementation; developer enablement and training; AI governance; legacy modernization; distributed/offshore engineering teams; public-sector software delivery.

Ideal Candidate A senior software engineer or architect who has evolved into an AI engineering practitioner and transformation consultant. The candidate should be able to open a real repository, understand the application, use AI to analyze and modify code, generate tests, perform a review, and teach the team how to repeat the process. The ideal consultant is equally comfortable: Teach Demonstrate Implement Coach Measure.

Engagement & Success Measures

Initial engagement: 8-12 weeks, with potential extension. Sparient is open to part-time, fixed-duration, retainer, remote, or hybrid consulting and to individual consultants or specialized firms.

- Measure baseline and improvement in development effort, legacy-code understanding, test coverage, defect resolution, code-review cycle time, documentation quality, AI-tool adoption, automation, and developer experience.
- Success is defined by practical adoption and measurable engineering improvement not training attendance alone.

📌 AI-Enabled Software Development Lifecycle Consultant (Chennai)
🏢 Sparient Global
📍 Chennai