GenAI Architect

Define enterprise-wide Generative AI architecture strategy, standards, and best practices .

Design scalable and secure LLM-powered solutions using Azure OpenAI and Azure AI Services.

Architect enterprise-grade RAG platforms leveraging vector databases, semantic search, and knowledge management systems.

Design and govern Agentic AI ecosystems utilizing AI Agents, tool calling, orchestration, and autonomous workflows.

Evaluate and select appropriate foundation models, embeddings, and multimodal architectures based on business requirements.

Design AI platforms supporting: Enterprise Chatbots

Intelligent Search

Knowledge Assistants

Document Intelligence

Software Engineering Assistants

Business Process Automation

Define reference architectures for: Multi-agent systems

RAG pipelines

AI gateways

Model serving

Prompt management

Evaluation frameworks

Establish AI governance, Responsible AI, security, privacy, compliance, and risk management controls.

Architect solutions utilizing Vision LLMs, OCR, and multimodal AI capabilities.

Guide engineering teams on implementation patterns using LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and other AI frameworks.

Define observability, monitoring, evaluation, and feedback-loop mechanisms for AI applications.

Lead technical design reviews, architecture governance boards, and AI solution assessments.

Optimize AI solutions for scalability, reliability, latency, cost, and performance.

Define CI/CD and MLOps strategies for AI solution deployment.

Collaborate with enterprise architects, cloud architects, security teams, and business stakeholders to align AI initiatives with organizational goals.

Mentor AI engineers, data scientists, and development teams on modern GenAI architectures and engineering practices.

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

10+ years of overall software engineering and architecture experience.

5+ years of solution architecture experience in cloud-native environments.

3+ years of hands-on experience in Generative AI and LLM solution architecture.

Proven experience designing enterprise-scale RAG platforms .

Experience architecting AI Agent and multi-agent solutions.

Strong expertise with Azure OpenAI and Azure AI ecosystem.

Experience defining AI governance, security, and Responsible AI frameworks.

Hands-on experience with vector databases and semantic search solutions.

Experience designing multimodal AI applications involving text, image, and document intelligence.

Experience leading technical teams and driving architecture decisions across large organizations.

Experience implementing MLOps and AI platform engineering practices.

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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