AI Architect

Forward Deployed Engineer focuses on establishing enterprise architecture across data, applications and technology along with an AI augmented delivery model to translate the enterprise architecture into working product for the business value stream. It aims at orchestration across product managers, designers and developers to shape up the production-ready products that embeds Gen AI and Agentic AI capabilities for bigger business value as well as meeting security best practices (e.g. OWASP), compliance, performance and ethical standards for Operational, Digital, Analytical and AI use cases.

Forward Deployed Engineer will lay down the AI based building blocks of the solution with apt digital worker and context engineering-based development to augment the Application Engineers. As AI and agent-based delivery reshape software development, AI Forward Deployed Engineer increasingly collaborates with digital workers and intelligent agents and performs much bigger roles combining industry / domain expertise, application, integration, data and platform architectures.

Key Responsibilities

Your role and responsibilities

• Define the overall architecture, agents and delivery model
• Collaborate with the line of business of the client engagements and translate to a component model.
• Define data architecture , application architecture and technology architecture.
• Define and build the agents and assistants to be leveraged to build the product based on the architecture defined.
• Define the assets to be adopted during the SDLC lifecycle.
• Integration across various AI orchestration of Dev cycle
• Define the workflow of agents to augment the lifecycle development.
• Integrate agents and assistants across various SDLC activities . Leverage the relevant assets
• Work with full stack engineers to define data models , high level design for implementation.
• Define AI based static code coverage and unit test coverage.
• Define AI based testing cycle
• Define a deployment model
• Define a deployment model and a devops pipeline
• Define AI augmentation across the model

Required Skills

• Define the enterprise architecture for digitalization of business, with understanding across business value stream, supplementing the same with a data model, to build applications to cater to the value stream.
• Define the AI augmented engine across SDLC lifecycle. Adopt AI solution across enterprise. AI coding engine adoptions across the life cycle.
• Define standards of context engineering for code development.
• Take Architectural decisions on a business application development on technology, data source and AI Agent adoption. Define ethical AI adoption in client enterprise.
• Familiarity with MCP servers, tool connectors, and LLM orchestration frameworks.
• Experience building agentic AI applications and LLM-powered workflows.
• Document usage of relevant assets on ADDs
• Define a CICD pipeline for deployment of code across the applications.
• Build an IaC based approach for deployment
• Define adoption of assistants, AI augmented coding tactics and Gen AI tools across phases of SDLC life cycle.
• Work with enterprise integration architects on system integration and also the integration of agents across the enterprise.

• One of the following:
• TOGAF Level 2 certification
• OR
• OpenGroup Master Certified Architect

Coding & Development Agents

• GitHub Copilot
• Roo/Cline
• Cursor
• Visual Studio Code with AI-assisted development workflows
• ChatGPT for code generation, debugging, and documentation

• Miro with AI-assisted ideation and workflow mapping
• Jira for Agile delivery and AI-supported backlog management
• Confluence for AI-enhanced documentation and knowledge sharing

• LeanIX
• AI-powered SDLC assistants and workflow automation tools
• Experience leveraging AI assistants for requirements analysis, documentation generation, testing support, and delivery acceleration