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Agentic AI Architect - LLM/RAG

Big Ideas Social Media Recruitment
12 - 15 Years
Multiple Locations

Posted on: 09/07/2026

Job Description

About the Role :

The Agentic AI Architect will lead the design and architecture of enterprise-scale Agentic AI platforms that enable autonomous, intelligent, and collaborative AI agents. This role is responsible for defining technology strategy, architecture standards, reference frameworks, and engineering best practices while ensuring scalable, secure, and production-ready AI solutions. The architect will work closely with business stakeholders, AI leaders, product teams, and engineering organizations to transform business problems into intelligent AI-driven solutions.

Key Responsibilities :

- Define the enterprise architecture and technical roadmap for Agentic AI platforms.

- Design scalable multi-agent systems capable of reasoning, planning, memory management, collaboration, and autonomous decision-making.

- Architect AI solutions using LLMs, RAG, vector databases, embeddings, prompt engineering, orchestration frameworks, and agent execution engines.

- Design reusable frameworks for agent lifecycle management, orchestration, observability, governance, and monitoring.

- Build integration patterns with enterprise systems including ERP, CRM, SCM, WMS, APIs, databases, messaging systems, and cloud platforms.

- Define secure AI architecture covering authentication, authorization, encryption, compliance, privacy, guardrails, and responsible AI practices.

- Evaluate and recommend Agentic AI frameworks including Google ADK, LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, MCP, and similar technologies.

- Lead architecture reviews, design workshops, technical governance, and solution validation sessions.

- Develop proof-of-concepts and innovation accelerators to evaluate emerging AI technologies.

- Mentor architects and engineers while establishing engineering standards and coding best practices.

- Collaborate with Product, Data Science, ML Engineering, Cloud, DevOps, and Security teams throughout the delivery lifecycle.

- Drive AI platform modernization, optimization, scalability, and cost efficiency.

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