Key Responsibilities

• Solution Architecture: Define end-to-end architecture for full-stack enterprise applications integrating AgentAI copilots, pre-built AI services, and APIs. Ensure compliance with SDLC, security, and performance standards while embedding AI-assisted workflows.
• AI Integration: Use AgentAI tools and LLM-based copilots for coding, testing, debugging, documentation, and design acceleration. Incorporate AI services (e.g., RAG-based retrieval, conversational APIs) into enterprise systems.
• Full-Stack Design: Architect scalable front-end and back-end solutions using modern frameworks and cloud-native patterns. Optimize API design, data flows, and UI/UX for AI-assisted features.
• Collaboration: Act as a liaison between traditional engineering teams and AI-native squads drive adoption of AI-assisted development practices.
• Governance & Quality: Maintain architectural integrity, scalability, and reliability enforce responsible AI usage and guardrails.

Must-Have Qualifications

Solid foundation in enterprise engineering, SDLC, and domain/industry workflows. Demonstrated capability in using copilots and LLM tools for software development tasks. Practical understanding of AI concepts like RAG and limitations of LLMs. Ability to deliver faster, higher-quality outcomes by combining human judgment with AI-assisted productivity. Does not train models or design ML architectures focuses on integration and productivity uplift.

Programming & Full-Stack Skills

Backend (Any 1 or more):

• Java (Spring Boot) - microservices, API integration
• .NET (C#) - enterprise application frameworks
• Python - scripting, automation, AI API integration

Frontend:

• React / Angular / Vue.js - responsive UI development
• TypeScript / JavaScript - modern front-end engineering

Additional: SQL/NoSQL, REST/GraphQL APIs, cloud-native skills (Azure/AWS/GCP), containerization (Docker/Kubernetes), CI/CD pipelines.