GenAI Architect

Key Responsibilities

1. Define enterprise-wide Generative AI architecture strategy, standards, and best practices.
2. Design scalable and secure LLM-powered solutions using Azure OpenAI and Azure AI Services.
3. Architect enterprise-grade RAG platforms leveraging vector databases, semantic search, and knowledge management systems.
4. Design and govern Agentic AI ecosystems utilizing AI Agents, tool calling, orchestration, and autonomous workflows.
5. Evaluate and select appropriate foundation models, embeddings, and multimodal architectures based on business requirements.
6. Design AI platforms supporting: 
   • Enterprise Chatbots
   • Intelligent Search
   • Knowledge Assistants
   • Document Intelligence
   • Software Engineering Assistants
   • Business Process Automation
7. Define reference architectures for: 
   • Multi-agent systems
   • RAG pipelines
   • AI gateways
   • Model serving
   • Prompt management
   • Evaluation frameworks
8. Establish AI governance, Responsible AI, security, privacy, compliance, and risk management controls.
9. Architect solutions utilizing Vision LLMs, OCR, and multimodal AI capabilities.
10. Guide engineering teams on implementation patterns using LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and other AI frameworks.
11. Define observability, monitoring, evaluation, and feedback-loop mechanisms for AI applications.
12. Lead technical design reviews, architecture governance boards, and AI solution assessments.
13. Optimize AI solutions for scalability, reliability, latency, cost, and performance.
14. Define CI/CD and MLOps strategies for AI solution deployment.
15. Collaborate with enterprise architects, cloud architects, security teams, and business stakeholders to align AI initiatives with organizational goals.
16. Mentor AI engineers, data scientists, and development teams on modern GenAI architectures and engineering practices.
17. Stay current with emerging AI technologies, models, frameworks, and industry trends.

Required Skills

Generative AI & LLMs

• Large Language Models (LLMs)
• GPT-4o, GPT-4.1, Claude, Gemini, Llama
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic Search
• AI Agents
• Function Calling
• Context Engineering
• Memory Management
• Model Evaluation
• Fine-Tuning Concepts
• Multi-Agent Systems
• Agent Orchestration

Agentic AI Frameworks

• LangGraph
• LangChain
• LlamaIndex
• CrewAI
• AutoGen
• Semantic Kernel
• OpenAI SDK
• MCP (Model Context Protocol)

Cloud & AI Platforms

• Azure OpenAI Service
• Azure AI Foundry
• Azure AI Search
• Azure AI Services
• Azure Document Intelligence
• Azure Functions
• Azure Kubernetes Service (AKS)
• Azure API Management
• Azure Storage
• Azure Key Vault
• Azure Monitor

Architecture & Enterprise Design

• Enterprise Architecture Frameworks
• Solution Architecture
• Cloud-Native Architecture
• Event-Driven Architecture
• API-Led Architecture
• Microservices Design
• High Availability Design
• Scalability Engineering
• Security Architecture
• Integration Architecture

Data & Vector Technologies

• Azure AI Search
• FAISS
• Pinecone
• Weaviate
• ChromaDB
• PostgreSQL
• Cosmos DB
• Redis
• Knowledge Graphs
• Data Modeling

Programming & Development

• Python (Expert)
• FastAPI
• REST APIs
• Asynchronous Programming
• Docker
• Kubernetes
• GitHub
• Azure DevOps
• CI/CD Pipelines

Observability & Evaluation

• Opik
• LangSmith
• MLflow
• Prompt Evaluation
• LLM Monitoring
• Experiment Tracking
• AI Quality Metrics
• Hallucination Detection
• Performance Benchmarking

Security & Governance

• Responsible AI
• AI Governance Frameworks
• Data Privacy
• Model Security
• Prompt Injection Protection
• AI Risk Management
• Compliance Controls
• Enterprise Security Standards

Required Experience

1. 10+ years of overall software engineering and architecture experience.
2. 5+ years of solution architecture experience in cloud-native environments.
3. 3+ years of hands-on experience in Generative AI and LLM solution architecture.
4. Proven experience designing enterprise-scale RAG platforms.
5. Experience architecting AI Agent and multi-agent solutions.
6. Strong expertise with Azure OpenAI and Azure AI ecosystem.
7. Experience defining AI governance, security, and Responsible AI frameworks.
8. Hands-on experience with vector databases and semantic search solutions.
9. Experience designing multimodal AI applications involving text, image, and document intelligence.
10. Experience leading technical teams and driving architecture decisions across large organizations.
11. Experience implementing MLOps and AI platform engineering practices.
12. Strong stakeholder management and client-facing consulting experience.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• Microsoft Certified: 
  • Azure Solutions Architect Expert
  • Azure AI Engineer Associate
  • Azure AI Fundamentals
• Experience working with Fortune 500 enterprise AI transformation programs.
• Experience with GenAI governance and AI Center of Excellence (CoE) initiatives.
• Knowledge of FinTech, Healthcare, Insurance, Retail, or Banking domain AI implementations.

Nice-to-Have Skills

• NVIDIA NIM
• Hugging Face Ecosystem
• GraphRAG
• Neo4j
• Databricks
• Snowflake Cortex
• Vertex AI
• Amazon Bedrock
• OpenTelemetry
• AI Gateway Platforms
• Federated AI Architectures
• Knowledge Graph Solutions
• Vector Database Benchmarking
• Enterprise CoPilot Development
• AI Platform Engineering

Experience Range

12-18+ Years

Target Profile

This role is suitable for a GenAI Solution Architect / AI Architect / Enterprise AI Architect who can define enterprise AI strategy, architect scalable GenAI platforms, govern AI adoption, and lead large-scale AI transformation initiatives beyond hands-on development.

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OK