Gen AI Architect

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.