Role Purpose

TGC is transitioning its software engineering environment toward an Agentic-AI-first development model, where AI agents participate across requirements, architecture, frontend and backend development, testing, security, deployment and ongoing application maintenance.

We are looking for a hands-on Agentic AI Solution Architect who can architect, implement and govern this engineering workplace.

This is not a conventional Solution Architect or Generative AI role. The successful candidate must be capable of using AI coding agents to build and deploy real production applications while establishing the architecture, guardrails and development practices required for engineering teams to work effectively with agents.

Key Responsibilities

Agentic Engineering Architecture

- Design TGC's end-to-end Agentic Software Development Lifecycle (A-SDLC).

- Build and configure specialized agents for frontend, backend, database, QA, security and DevOps activities.

- Design effective system prompts, context engineering, tool access, agent memory and orchestration.

- Establish agent-to-agent workflows and human approval gates.

- Create reusable engineering instructions and standards across TGC products.

Application Architecture

Own and guide architecture across:

- Angular and TypeScript

- Angular Micro-Frontend architecture

- .NET / ASP.NET Core

- REST APIs

- Microservices

- SQL and NoSQL databases

- Azure cloud services

- API integrations

- Authentication, authorization and RBAC

- Scalable multi-tenant SaaS architecture

Agentic Development

The role must be highly hands-on.

The candidate should be able to take:

Requirement → Architecture → Agent Instructions → Generated Code → Testing → Security Validation → Deployment without defaulting to manual coding whenever an agent encounters a problem.

When agents fail, the individual should diagnose whether the failure resulted from context, instructions, architecture, tooling, permissions,



test coverage or model limitations and improve the agentic system accordingly.

DevOps & Deployment

- Design AI-assisted CI/CD workflows.

- Implement automated build, testing and deployment.

- Work with Azure DevOps/GitHub pipelines.

- Implement Infrastructure as Code where appropriate.

- Manage Azure Key Vault and application secrets securely.

- Establish automated rollback, logging and monitoring.

Engineering Governance

Create and enforce:

- Agent-first development standards

- Coding and architecture standards

- Automated testing requirements

- AI-generated code review processes

- Security validation

- Human approval gates

- Production deployment controls

- Documentation and traceability of agent-generated changes

Team Enablement

A major responsibility will be helping TGC's existing engineering team transition from conventional development to AI-native engineering.

Essential Technical Skills

Strong hands-on experience with:

- Agentic AI / AI coding agents

- LLM system prompting and context engineering

- Tool/function calling and agent orchestration

- MCP or equivalent agent-tool integration

- Git/GitHub

- Angular / TypeScript

- .NET / ASP.NET Core

- REST APIs and microservices

- SQL / database architecture

- Microsoft Azure

- Docker / containers

- CI/CD

- Automated testing

- Application security

Experience with modern AI development environments such as Claude Code, GitHub Copilot, Cursor or equivalent agentic coding platforms is highly desirable.

The Person We Are Looking For

We are specifically looking for someone who thinks:

"How do I make the agent solve this reliably and repeatedly?"

rather than:





"I'll just fix the code myself."

The ideal candidate is:

- Architecturally strong

- Extremely hands-on

- Comfortable experimenting with rapidly evolving AI technology

- Strong at debugging AI-agent behaviour

- Able to distinguish AI limitations from poor instructions or architecture

- Security conscious

- Product and UX aware

- Comfortable challenging traditional software-development practices

- Capable of teaching other engineers how to work effectively with AI agents

Selection Process

Candidates should expect a practical assessment.

They may be provided with a partially functioning application and access to an AI coding environment and asked to:

Diagnose, correct, test and deploy the application primarily through AI agents rather than manually rewriting the code.

We are interested not simply in whether the candidate solves the problem, but how they design, instruct, supervise and improve the agentic engineering process used to solve it.

Success in the First 90 Days

The successful candidate should:

- Establish TGC's Agentic Development Architecture.

- Implement the Agentic SDLC.

- Create reusable FE, BE, QA, Security and DevOps agents.

- Establish automated development and deployment pipelines.

- Introduce measurable agentic engineering standards.

- Reduce dependence on manual coding.

- Improve development velocity without compromising security or quality.

- Transition the engineering team toward an AI-native development model.

Core Mandate

Build the engineering system that enables AI agents and humans together to deliver secure, scalable production software faster and more reliably.

Work Location Requirement: This is a full-time, work-from-office position based in Pune. Candidates must be willing to work from the Pune office; hybrid and remote working are not applicable to this role

Pay: ₹682,000.00 - ₹770,000.00 per year

Work Location: In person

📌 Agentic AI Solution Architect (Pune)
🏢 TGC Technologies
📍 Pune