NTT DATA, Inc.
Agentic Ai Lead
Skills
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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