Data & Business Insights Analyst

Job Title: AI Innovation Architect

Job Summary

AI Innovation Architect

LLM integration

MCP protocol

RAG architecture

AI-driven application design

Key Responsibilities

• Architect and lead the development of scalable, secure, and high-performance AI solutions using LLMs, RAG, and MCP.
• Define and implement strategies for LLM model selection, prompt engineering, tool calling, and chunking optimization.
• Collaborate with engineering teams to integrate AI capabilities into frontend (React, TypeScript) and backend (Python, FastAPI) systems.
• Guide the implementation of OAuth 2.0, API-based system integrations, and cloud-native deployments (preferably on GCP).
• Work closely with QA, DevOps, and delivery teams to ensure end-to-end solution quality and performance.
• Engage with stakeholders to understand business needs and translate them into innovative AI architectures.
• Provide technical leadership and mentorship across cross-functional teams.
• Stay ahead of emerging AI trends and evaluate new tools, frameworks, and platforms for enterprise adoption.
  

Qualifications

• 10+ years of experience in software architecture and solution design.
• Proven experience in:
• Generative AI, LLM integration, and RAG implementation.
• MCP protocol, tool calling, and prompt engineering.
• Python, FastAPI, and OAuth 2.0 for backend development.
• React, TypeScript, and Tailwind CSS for frontend integration.
• Strong understanding of cloud platforms, preferably Google Cloud Platform (GCP).
• Experience in Agile delivery environments and stakeholder engagement.
• Educational background: 10 + 2 + 4 (Bachelor’s degree in Computer Science, Engineering, or related field).
  

Key Competencies

• AI & System Architecture: Expertise in designing AI-first systems with scalable and modular architecture.
• Innovation Leadership: Ability to drive innovation and influence technical direction across teams.
• Stakeholder Management: Strong communication and expectation-setting skills with technical and business stakeholders.
• Agile Execution: Experience leading Agile teams and delivering iterative value.
• End-to-End Delivery: Proven ability to oversee architecture, development, testing, and deployment of AI solutions.