Role Summary
We are seeking a highly experienced AI Architect to design, govern, and deliver scalable enterprise AI platforms and solutions. The role requires deep expertise in LLMs, Agentic AI frameworks (LangChain, LangGraph, AutoGen), MCP Servers, Vector Databases, and end‑to‑end AI system architecture across cloud and on‑prem environments. The candidate will translate business goals into robust AI architectures while ensuring security, scalability, observability, and cost efficiency.
Key Responsibilities
AI & Solution Architecture
- Define enterprise AI reference architectures for LLM, RAG, and Agentic AI use cases
- Experience designing and operating Model Context Protocol (MCP)–based integrations to standardize AI‑to‑tool communication across enterprise systems.
- Hands‑on knowledge of MCP Server and MCP Gateway patterns for secure tool exposure, access control, and scalable agent interoperability across platforms.
- Design end‑to‑end GenAI pipelines including ingestion, embedding, retrieval, orchestration, and response generation
- Architect multi‑agent systems using LangChain, LangGraph, AutoGen for workflow automation and decision intelligence
- Design and implement Agentic AI frameworks (planner–executor, tool‑calling, memory, guardrails)
- Hands‑on experience with Pinecone, FAISS, Azure AI Search, OpenSearch
- Design AI platforms on Azure / AWS / GCP (Native + Managed AI services)
- Integrate with Azure OpenAI / AWS Bedrock / Open‑source LLMs
- Implement human‑in‑the‑loop, safety controls, and agent governance
- Design embedding strategies, indexing, hybrid search (vector + keyword), and metadata filtering
- Define AI governance, model lifecycle, versioning, and auditability
- Lead architecture design reviews and enforce AI architecture standards
- Act as AI technical authority for business and leadership stakeholders
- Mentor engineers and architects on GenAI and Agentic patterns
- Support RFPs, proposals, and executive architecture presentations
Required Experience & Skills
Core Experience
- 15+ years in software, data, platform, or enterprise architecture roles with progressive ownership of large‑scale systems
- 7+ years designing and deploying AI/ML solutions in production environments
- 5+ years leading GenAI / LLM architecture, including RAG, agentic workflows, and AI platforms
- Proven experience architecting enterprise‑grade AI platforms with Agentic workflow engines and decision automation
- Experience in LLM evaluation frameworks and guardrails
- Proven experience deploying LLM‑based and RAG solutions at scale
Technical Skills
- Agentic AI: LangChain, LangGraph, AutoGen
- MCP Servers, MCP Gateway
- Vector Databases: Pinecone, FAISS, Azure AI Search
- LLMs: GPT‑4/4o, Claude, LLaMA, Mistral
- Programming: Python (strong)
- Cloud: Azure / AWS (AI services, IAM, networking)
- Containers & Orchestration: Docker, Kubernetes
- Data: Structured, unstructured, document pipelines (OCR, NLP)
- Certifications in Cloud, AI, or Enterprise Architecture
