Job Description for AI Solution Architect Role Summary:

Design and lead end-to-end AI and Generative AI solutions that align with business goals, ensuring scalability, security, governance, and measurable business value. Act as the bridge between business stakeholders, data scientists, developers, and enterprise architecture teams.

Key Responsibilities

Define AI, ML, and Generative AI solution architectures.

Design and implement LLM, RAG, AI Agent, and Copilot-based solutions.

Translate business requirements into scalable technical solutions.

Lead AI platform, cloud, and integration architecture decisions.

Establish AI governance, security, compliance, and Responsible AI practices.

Drive MLOps/LLMOps, model deployment, monitoring, and lifecycle management.

Provide technical leadership, mentoring, and architecture governance.

Evaluate emerging AI technologies and recommend adoption strategies.

Required Qualifications

- Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
- 8+ years of IT/Solution Architecture experience.
- 3+ years of AI/ML and Generative AI solution delivery experience.
- Experience delivering enterprise-scale cloud solutions.





Technical Skills

AI & GenAI

Machine Learning, Deep Learning, NLP

Large Language Models (GPT, Claude, Gemini, Llama)

RAG, Prompt Engineering, AI Agents

Azure OpenAI, Azure AI Studio

Cloud & Architecture

Microsoft Azure (preferred), AWS, or GCP

Microservices, APIs, Event-Driven Architecture

Enterprise Integration Patterns

Development & Operations

Python, C#, Java, or JavaScript

GitHub, Azure DevOps

Docker, Kubernetes

CI/CD, MLOps, LLMOps

Preferred Certifications

Microsoft Certified: Azure Solutions Architect Expert

Microsoft Certified: Azure AI Engineer Associate

AWS Machine Learning Specialty

TOGAF

Key Competencies

Strategic Thinking

Technical Leadership

Stakeholder Management

Problem Solving

Communication & Presentation

Innovation & Continuous Learning

Success Measures

Successful delivery of AI solutions aligned to business outcomes.

High adoption and measurable value realization.

Secure, scalable, and compliant AI implementations.

Reduced time-to-market through reusable AI architecture patterns.

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