Agentic AI Architect

pinnacleu

pune 5 Years Exp Posted 58d ago

Job Description

System Architecture & Design

  • Define the end-to-end agentic AI architecture — LLM orchestration layers, agent coordination mechanisms, memory management, tool-calling patterns, and multi-agent workflows
  • Design multi-agent orchestration patterns including hierarchical agents, supervisor-worker topologies, plan-and-execute strategies, and agent-to-agent (A2A) communication
  • Architect RAG (Retrieval-Augmented Generation) pipelines, hybrid search, and knowledge management systems tailored to higher-education content (catalogs, policies, student data)
  • Establish architectural standards for prompt engineering, context-window management, token budgeting, and evaluation frameworks
  • Design for non-functional requirements: latency targets, throughput, reliability, observability, cost controls, and scaling guardrails

Production Engineering Standards

  • Build production-grade systems with structured error handling, retry logic, circuit breakers, and fallback execution paths
  • Define DevSecOps architecture and deployment patterns across multi-cloud environments.
  • Implement human-in-the-loop (HITL) checkpoints for sensitive decision points within student journeys
  • Establish monitoring, observability, and audit logging frameworks for compliance, traceability, and agent behavior analysis
  • Design secure AI architectures — threat modeling, adversarial prompt defenses, and data privacy controls aligned with FERPA and SOC 2

Client & Team Communication

  • Produce clear architecture documentation: reference diagrams, integration patterns, decision records, ADRs, and runbooks for production operations
  • Communicate architectural trade-offs to both engineering teams and non-technical enterprise clients in a structured, accessible way
  • Lead architecture and design reviews, mentoring engineers on agentic patterns and production best practices
  • Serve as the technical point of contact for enterprise client integrations — translating institutional requirements into AI system design

Research & Innovation

  • Stay current with the latest agent frameworks (LangGraph, CrewAI, AutoGen, Google ADK), LLM advancements, and inference optimization techniques
  • Build prototypes and proof-of-concepts to validate architectural approaches before committing to production builds
    • Drive the technology roadmap for Our Client's agentic platform — identifying where autonomous agents can replace manual workflows with measurable ROI

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