Agentic AI Architect

The Role

Your role Defines Agentic AI solution architecture, platform patterns, integration strategy, governance, security controls, and scalable multi-agent design. Ensures alignment with enterprise standards and production readiness.

AI Strategy Architecture

• Define enterprise Agentic AI architecture and technology roadmap.

• Design scalable, modular, and reusable AI agent frameworks.

• Establish architectural standards, governance, and best practices for AI solutions.

• Evaluate emerging AI technologies and recommend adoption strategies.

• Drive AI modernization initiatives across business units.

Agentic AI Solution Design

• Design autonomous AI agents capable of planning, reasoning, memory management, tool usage, and task execution.

• Architect multi-agent collaboration patterns for complex workflows.

• Design orchestration mechanisms for agent communication and coordination.

• Define agent lifecycle management including planning, execution, reflection, and optimization.

• Build reusable AI capabilities across multiple enterprise use cases.

LLM GenAI Architecture

• Architect solutions leveraging foundation models such as GPT, Claude, Gemini, Llama, Mistral, and enterprise-hosted models.

• Design Retrieval-Augmented Generation (RAG) architectures.

• Develop prompt engineering and prompt management strategies.

• Design semantic search using vector databases.

• Optimize AI inference performance, latency, and operational costs.

AI Platform Engineering

• Architect scalable AI platforms on Azure, AWS, or Google Cloud.

• Design APIs and microservices supporting AI workloads.

• Integrate AI agents with enterprise systems such as SAP, ServiceNow, Salesforce, Microsoft 365, Jira, Slack, and internal applications.

• Build reusable AI accelerators and reference architectures.

• Define CI/CD pipelines for AI applications (LLMOps/MLOps).

Data Knowledge Architecture

• Design enterprise knowledge ingestion pipelines.

• Architect document processing, embeddings, vector indexing, and metadata management.

• Define strategies for structured and unstructured data integration.

• Design knowledge graphs and semantic retrieval architectures where appropriate.

Governance, Security Responsible AI

• Establish Responsible AI guidelines and governance.

• Ensure compliance with enterprise security policies and regulatory standards.

• Implement guardrails against hallucinations, prompt injection, and data leakage.

• Design authentication, authorization, audit logging, and AI monitoring frameworks.

• Ensure explainability, transparency, and ethical AI practices.

Leadership Collaboration

• Lead architecture reviews and technical design sessions.

• Mentor architects, AI engineers, and development teams.

• Partner with business leaders to identify AI transformation opportunities.

• Drive proof-of-concepts through production deployment.

Required Technical Skills

Artificial Intelligence

• Large Language Models (LLMs)

• Agentic AI

• Multi-Agent Systems

• Autonomous AI Agents

• Retrieval-Augmented Generation (RAG)

• Prompt Engineering

• AI Reasoning Planning

• AI Memory Architectures

• AI Evaluation Frameworks

Frameworks

• LangChain

• LangGraph

• Microsoft AutoGen

• CrewAI

• LlamaIndex

• OpenAI SDK

Cloud Platforms

• Microsoft Azure

• Azure OpenAI Service

• AWS Bedrock

• Google Vertex AI

• Kubernetes

• Docker

Programming

• Python

• Java

• C#

• JavaScript/TypeScript

• REST APIs

• FastAPI

• .NET

Data Technologies

• SQL

• NoSQL

• Vector Databases (Pinecone, Milvus, Weaviate, Azure AI Search, ChromaDB)

• Elasticsearch

• Cosmos DB

DevOps AI Operations

• GitHub

• Azure DevOps

• Jenkins

• Terraform

• CI/CD

• MLflow

• LangSmith

• Azure AI Foundry

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.