Agentic Architect

About the Role We are seeking an experienced AI Agentic Solutions Architect to design and lead enterprise-scale agentic AI platforms. This role is focused on architecting advanced multi-agent systems, LLMOps platforms, RAG solutions, and Responsible AI frameworks while establishing engineering standards for scalable, secure, and production-ready AI applications. Key Responsibilities Define enterprise architecture standards for agentic AI, including agent design patterns, deployment topology, evaluation strategies, and Responsible AI governance. Design and implement advanced multi-agent systems with supervisor hierarchies, agent delegation, shared memory, agent-to-agent communication, and orchestration workflows. Architect scalable Retrieval-Augmented Generation (RAG) platforms using Azure AI Search, vector databases, GraphRAG, and Knowledge Graph technologies. Build and optimize LLMOps platforms using PromptFlow, LangSmith, model evaluation frameworks, AI observability, and cost optimization strategies. Design Responsible AI frameworks, including guardrails, content safety, bias detection, audit logging, and governance controls. Develop evaluation frameworks with offline benchmarks, online monitoring, A/B testing, and human feedback mechanisms. Collaborate with Data Engineering teams to design data pipelines, knowledge bases, and fine-tuning datasets for AI solutions. Drive architecture reviews, establish engineering best practices, and mentor development teams on enterprise AI standards. Ensure production resilience through model routing, fallback strategies, checkpointing, token optimization, and multi-provider LLM deployments. Key Skills & Qualifications 10+ years of software engineering experience with strong expertise in Python and Java or .NET. Deep architectural expertise in LangChain, LangGraph, and enterprise AI application development. Hands-on experience with Semantic Kernel, CrewAI, AutoGen, and LlamaIndex. Strong knowledge of Azure AI Search, Vector Databases, GraphRAG, Neo4j, and Knowledge Graph architectures. Experience building enterprise RAG platforms and AI knowledge retrieval systems. Expertise in PromptFlow, LangSmith, AI observability, model evaluation, and LLMOps best practices. Strong understanding of Responsible AI principles, including fairness, bias detection, explainability, and governance. Experience with model routing, token budgeting, rate-limit handling, multi-provider LLM architectures, and production AI operations. Experience deploying scalable AI solutions on AWS or Azure (cloud certification preferred). Excellent technical leadership, architecture review, stakeholder management, and mentoring skills. Preferred Skills Experience with Model Context Protocol (MCP) and AI tool ecosystem governance. Knowledge of prompt injection prevention, secure agent execution, and AI security best practices. Experience with fine-tuning techniques including LoRA, RLHF, and DPO. Familiarity with MLflow for experiment tracking and model lifecycle management. Experience using AI-assisted development tools such as Claude Code and Cursor. Strong understanding of Architecture Decision Records (ADRs) and enterprise design governance. Apply by sending your CV to careers@cliqhr.co.in