Agentic AI Architect

Job SummaryWe are seeking an experienced Agentic AI Architect to design and build enterprise-scale Agentic AI platforms that enable autonomous, intelligent, and collaborative AI agents. The ideal candidate will drive architecture, technology strategy, and AI engineering best practices while delivering scalable, secure, and production-ready AI solutions.Key ResponsibilitiesDesign enterprise architecture and technical roadmap for Agentic AI platforms.Build scalable multi-agent AI systems with reasoning, planning, memory, and autonomous decision-making capabilities.Architect solutions using LLMs, RAG, vector databases, embeddings, prompt engineering, orchestration frameworks, and agent execution engines.Design reusable frameworks for agent orchestration, lifecycle management, governance, observability, and monitoring.Integrate AI platforms with enterprise applications, APIs, databases, messaging systems, and cloud services.Implement AI security, governance, privacy, compliance, guardrails, and Responsible AI practices.Evaluate and implement frameworks such as Google ADK, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and MCP.Lead architecture reviews, technical governance, design workshops, and solution validation.Develop Proof of Concepts (PoCs) to evaluate emerging AI technologies.Mentor engineering teams and establish architecture standards and best practices.Collaborate with Product, AI/ML, Data Engineering, Cloud, DevOps, and Security teams.Drive AI platform scalability, modernization, performance optimization, and cost efficiency.Required SkillsStrong expertise in Agentic AI architecture and enterprise AI solution design.Hands-on experience with Large Language Models (GPT, Gemini, Claude, Llama, Mistral).Strong knowledge of RAG, Vector Databases, Embeddings, Prompt Engineering, AI Memory, Knowledge Graphs, and AI Orchestration.Experience with Google ADK, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, MCP, or similar frameworks.Strong Python programming skills.Experience with FastAPI, REST APIs, Microservices, Distributed Systems, Event-Driven Architecture, Docker, Kubernetes, CI/CD, Git, and Azure/AWS/GCP.Knowledge of MLOps, LLMOps, AI Observability, monitoring, and production deployments.Strong understanding of AI Security, Governance, Responsible AI, and Enterprise Architecture.QualificationsMaster's or Ph.D. in Computer Science, Artificial Intelligence, Software Engineering, or a related field.10+ years of software engineering experience.5–7+ years of experience designing enterprise AI/ML platforms and distributed systems.Proven experience architecting production-grade Agentic AI solutions.Preferred ExperienceDomain experience in Supply Chain, Manufacturing, Retail, Healthcare, Financial Services, or Enterprise Automation.Experience building multi-agent collaboration systems.Knowledge of AI Governance and Responsible AI.Enterprise Architecture and Cloud Certifications are preferred.