Principal Agentic AI Engineer / Hands-on Technical Lead

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

We are currently seeking a Principal Agentic AI Engineer / Hands-on Technical Lead to join our team in Hyderabad, Telangana (IN-TG), India (IN).

• Principal Agentic AI Engineer / Hands-on Technical Lead

Agent applicability | Technical leadership | Hands-on engineering | Production delivery Number of positions 1

Level Principal Engineer / Hands-on Technical Lead

Primary locations Hyderabad or Noida preferred; exceptional onshore candidates may be considered

Target / alternate titles Principal AI Engineer; Staff Agentic AI Engineer; Lead GenAI Engineer; Principal LLM Engineer; Hands-on AI Solution Lead; Lead AI Application Engineer

Core keywords agentic AI, LLM, GenAI, business-process decomposition, agent patterns, MCP, tool calling, context engineering, RAG, workflow orchestration, Python, Java, TypeScript, APIs, microservices, cloud, Kubernetes, CI/CD, evaluation, observability

Recruiter red flags Architecture-only or advisory-only profile; no recent coding; prototype-only experience; cannot explain personal code contribution; framework evangelism; weak production troubleshooting; treats every use case as an agent.

Role purpose Lead a compact Agentic AI SWAT team that evaluates business processes, selects the correct agentic or non-agentic solution pattern, and delivers production-grade implementations within an established enterprise AI ecosystem. This is a hands-on technical leadership role: the individual is expected to design, code, review, troubleshoot, and deploy alongside the engineering team rather than operate only as an architect or advisor.

Client and delivery context

• The client already has enterprise AI infrastructure, production MCP capabilities, security and governance controls, and delivery pipelines. The lead must plug into those capabilities and accelerate execution.
• The pod may work across multiple business domains rather than one fixed function. The lead must rapidly understand new processes and guide two or more delivery tracks when needed.
• The role must balance rapid implementation with reusable engineering patterns, operational reliability, enterprise controls, and knowledge transfer to internal teams.
• The lead should be language-, cloud-, model-, and framework-agnostic and able to work with the client’s existing technology choices.

Primary Ownership

• Process-to-solution assessment, including whether a requirement calls for an agent, deterministic workflow, retrieval, conventional application logic, or a hybrid pattern.
• Technical direction for agents, MCP tools, context and memory, enterprise integrations, orchestration, evaluation, observability, and production readiness.
• Hands-on contribution to critical code paths, design spikes, integration patterns, code reviews, debugging, and release readiness.
• Reusable reference implementations, engineering standards, and knowledge-transfer assets that internal teams can replicate.

Key Responsibilities

• Work with business and engineering stakeholders to decompose end-to-end processes, identify decision points, data dependencies, controls, exceptions, and measurable outcomes.
• Determine where agentic AI is appropriate and define the operating pattern, autonomy boundary, tool set, context strategy, human oversight, and fallback behavior.
• Design and build production agents and supporting services, including APIs, microservices, event handlers, retrieval components, workflow logic, user-facing interfaces, and integration adapters.
• Define and review MCP server and tool patterns, tool schemas, authentication, authorization, error handling, versioning, and observability.
• Guide multi-agent and workflow orchestration, model selection or routing, state management, retries, timeouts, idempotency, queues, and exception management.
• Establish structured evaluation for task success, groundedness, tool correctness, safety, latency, cost, user acceptance, and regression prevention.
• Contribute directly to code, conduct code and design reviews, resolve complex technical issues, and help engineers move features through test and production environments.
• Align implementations with existing security, architecture, data, privacy, Responsible AI, a…