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Agentic Ai Lead

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Onsite

Agentic Ai Lead

NTT DATA, Inc.

Agentic Ai Lead

Skills

Google Cloud PlatformAIAWSNo-code AutomationsSalesforceSAP ERPServiceNowComplianceAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

10 August 2026

What they need


Agentic AI & Enterprise AI Development

  • Design and implement Agentic AI systems using modern orchestration frameworks.
  • Build autonomous and semi-autonomous AI agents capable of reasoning, planning, and tool use.
  • Design and optimize RAG architectures and enterprise knowledge retrieval systems.
  • Develop enterprise copilots utilizing Microsoft Copilot, Azure OpenAI, Claude, and other LLM ecosystems.
  • Implement multi-agent coordination architectures and workflow automation.

MCP (Model Context Protocol)

  • Design and integrate MCP-based solutions.
  • Build custom MCP servers and connectors.
  • Develop integrations across enterprise applications, databases, APIs, and collaboration platforms.
  • Implement secure context management and tool orchestration.

AI-Assisted SDLC & Spec-Driven Development

  • Lead AI-assisted software development practices across the SDLC.
  • Implement Spec-Driven Development methodologies.
  • Utilize Copilot, Claude Code, Cursor, and AI-assisted engineering tools.
  • Establish AI-assisted testing, documentation, code review, and deployment frameworks.
  • Define engineering standards for AI-native software delivery.

Data Governance & Lineage

  • Develop enterprise data governance frameworks.
  • Implement data quality, metadata management, and lineage tracking solutions.
  • Ensure compliance with security, privacy, regulatory, and responsible AI requirements.
  • Integrate governance capabilities across data and AI ecosystems.

Enterprise Architecture & Deployment

  • Architect scalable AI solutions on Azure/AWS/GCP.
  • Design API-driven and event-driven AI infrastructures.
  • Integrate AI services with enterprise platforms such as Microsoft 365, ServiceNow, Salesforce, SAP, and internal systems.
  • Optimize AI system reliability, observability, and scalability.

Stakeholder Engagement

  • Engage directly with business leaders, product teams, architects, and customers.
  • Conduct solution workshops and architecture reviews.
  • Translate business challenges into measurable AI outcomes.
  • Provide technical leadership and mentorship.

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