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Agentic AI Architect

  • Source: Naukri
  • Tata Consultancy Services
  • On-site
  • Bengaluru
  • Full-Time
  • Mid level

Job Description

We are seeking an experienced Agentic AI Architect to lead the design and implementation of enterprise-scale AI solutions powered by Generative AI, LLMs, Agentic AI, Multi-Agent Systems, and RAG architectures. The ideal candidate will architect intelligent AI ecosystems capable of autonomous reasoning, planning, decision-making, workflow orchestration, and business process automation while ensuring security, governance, scalability, and responsible AI practices.

Key Responsibilities

AI Solution Architecture

  • Design end-to-end Agentic AI architectures for enterprise business use cases.
  • Develop scalable, secure, and reusable AI architecture frameworks and patterns.
  • Define enterprise AI reference architectures, standards, and governance models.
  • Lead AI modernization and digital transformation initiatives.

Agentic AI & Multi-Agent Systems

  • Design autonomous AI agents capable of planning, memory management, reasoning, and task execution.
  • Build multi-agent orchestration frameworks using modern Agentic AI platforms.
  • Implement agent collaboration, tool calling, workflow automation, and decision-making capabilities.
  • Design Human-in-the-Loop (HITL) mechanisms for governance and validation.

Generative AI & LLM Engineering

  • Evaluate, deploy, and optimize LLMs including GPT, Claude, Gemini, Llama, Mistral, and open-source models.
  • Design advanced prompt engineering, prompt chaining, and agent workflow frameworks.
  • Architect Retrieval-Augmented Generation (RAG) solutions.
  • Implement semantic search, vector retrieval, embeddings, and knowledge systems.
  • Establish LLM evaluation and optimization strategies.

Cloud & AI Platform Engineering

  • Architect AI solutions using:

  • Azure OpenAI
  • Azure AI Foundry
  • AWS Bedrock
  • Amazon SageMaker
  • Google Vertex AI

  • Integrate AI agents with enterprise applications, APIs, data platforms, and business workflows.
  • Design event-driven and microservices-based AI architectures.

Data & Knowledge Management

  • Design enterprise knowledge repositories and AI knowledge management frameworks.
  • Implement vector databases including Pinecone, Weaviate, Chroma, Milvus, and Azure AI Search.
  • Define ingestion, chunking, indexing, embedding, and retrieval strategies.
  • Ensure data governance, lifecycle management, and knowledge quality standards.

AI Governance & Security

  • Define Responsible AI governance frameworks.
  • Implement AI guardrails, security controls, and compliance mechanisms.
  • Address risks related to hallucinations, prompt injection attacks, data privacy, and regulatory compliance.
  • Establish enterprise AI governance and model risk management practices.

LLMOps / AgentOps / MLOps

  • Design and implement AI deployment pipelines and automation frameworks.
  • Establish observability, monitoring, and AI performance evaluation frameworks.
  • Define AI KPIs, operational metrics, and continuous improvement processes.
  • Support production-scale AI operations and governance.

Required Skills

  • 8+ years of overall IT experience.
  • Minimum 3+ years of experience in Generative AI or AI Architecture roles.
  • Strong expertise in:

  • Agentic AI
  • Multi-Agent Systems
  • Generative AI
  • LLM Architectures
  • Machine Learning
  • Deep Learning
  • RAG Architectures

  • Hands-on experience with:

  • LangChain
  • LangGraph
  • Semantic Kernel
  • CrewAI
  • AutoGen
  • OpenAI APIs
  • Azure AI Foundry

  • Strong proficiency in:

  • Python
  • REST APIs
  • Microservices
  • Event-Driven Architecture

  • Experience working with:

  • Azure OpenAI
  • Azure AI Services
  • AWS Bedrock
  • Amazon SageMaker
  • Google Vertex AI

  • Experience with:

  • Kubernetes
  • Docker
  • CI/CD
  • Infrastructure as Code

Preferred Skills

  • Knowledge Graphs and semantic reasoning systems.
  • Enterprise Architecture and Digital Transformation experience.
  • AI Security and Responsible AI frameworks.
  • Data Governance and Regulatory Compliance.
  • LLMOps, AgentOps, and MLOps implementation experience.
  • Financial Services, Insurance, Healthcare, or Enterprise Platform experience.

Preferred Certifications

  • Microsoft Certified: Azure AI Engineer Associate
  • Microsoft Certified: Azure Solutions Architect Expert
  • AWS Certified Machine Learning Specialty
  • Google Professional Machine Learning Engineer
  • Databricks Generative AI Certification
  • TOGAF Certification
  • Certified Kubernetes Administrator (CKA)

Desired Candidate Profile

  • Strategic thi…

Skills

  • claude-by-anthropic
  • Gemini
  • meta-llama
  • llama
  • microsoft-azure
  • amazon-web-services
  • bedrock
  • amazon-sagemaker
  • vertex
  • chroma
  • Milvus
  • azure-search
  • guardrails
  • scaleai
  • LangChain
  • LangGraph
  • crewai
  • Python
  • Kubernetes
  • Docker
  • amazon-machine-learning
  • azure-databricks
  • databricks

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