Data & Business Insights Analyst

Job Title: AI Innovation Architect

Job Summary

AI Innovation Architect

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.