Lead AI Architect (Agentic AI)

Quick Facts

• Role: Lead AI Architect specializing in agentic AI and Generative AI

Description

You will design, develop, and implement scalable, intelligent, autonomous agentic and generative AI solutions for enterprise business applications. You will define the AI architecture, lead technical implementation, integrate AI agents with enterprise systems, and ensure scalability, security, reliability, and responsible AI use.

Responsibilities

• Design and implement agentic AI architectures for planning, reasoning, tool usage, and complex workflow execution
• Build single-agent and multi-agent systems for enterprise applications
• Architect AI orchestration, agent communication, memory management, and workflow automation
• Design human-in-the-loop and approval workflows for sensitive operations
• Lead LLM-powered enterprise application development
• Implement RAG (semantic search, embeddings, vector database usage)
• Optimize prompts, tool calling, structured outputs, and agent workflows
• Evaluate and integrate commercial and open-source LLMs
• Define enterprise AI architecture, technical standards, and implementation strategies
• Lead AI developers/ML engineers and cross-functional technical teams; perform architectural and code reviews and mentoring
• Translate business requirements into scalable AI solutions
• Build proof-of-concepts, prototypes, and production-ready AI applications
• Integrate AI agents with enterprise apps, databases, APIs, and business workflows
• Build API-driven architectures using Python, FastAPI, and microservices
• Design secure integrations with internal and third-party systems
• Implement event-driven and workflow-based AI automation
• Deploy and operate AI applications on AWS/Azure/GCP
• Establish evaluation, monitoring, observability, and performance optimization practices
• Implement guardrails, authentication, access controls, and data privacy protections
• Optimize LLM latency, token consumption, infrastructure costs, and response quality
• Implement testing and evaluation frameworks for agent performance and reliability

Requirements

• Agent architectures, tool calling, orchestration, and multi-agent workflows
• LLM expertise including prompt engineering, RAG, and embeddings
• Hands-on experience with AI orchestration frameworks (LangGraph, AutoGen, CrewAI, or equivalent)
• Strong Python development experience
• REST APIs, FastAPI, microservices, and enterprise integrations
• Vector database experience (Pinecone, Weaviate, Milvus, FAISS, or equivalent)
• Cloud deployment experience on AWS, Azure, or Google Cloud Platform
• Strong AI architecture/design experience covering solution design, distributed systems, scalability, and security
• Deployment with Docker and CI/CD for production AI applications
• Technical leadership including architecture reviews and stakeholder management
• LLM evaluation, agent testing, monitoring, and guardrails

Benefits

Not specified in the provided posting.