Senior Agentic Full-Stack Engineers

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 Senior Agentic Full-Stack Engineers to join our team in Noida/Gurgaon, Haryāna (IN-HR), India (IN).

2. Senior Agentic Full-Stack Engineer

Three openings | Agent and application engineering | End-to-end workflow implementation

Number of positions

3

Senior Individual Contributor

Hyderabad or Noida preferred; exceptional onshore candidates may be considered

Senior GenAI Engineer; Senior LLM Engineer; Senior AI Application Engineer; Full-Stack AI Engineer; Senior Software Engineer - Agentic AI; Applied AI Engineer

agentic AI, LLM applications, full-stack engineering, Python, Java, TypeScript, APIs, microservices, RAG, context engineering, tool calling, MCP, workflow automation, React, event-driven systems, testing, containers, Kubernetes, CI/CD, evaluation, observability

Prompt-only or notebook-only experience; no production application ownership; weak API or software-engineering fundamentals; cannot work outside one language or framework; no testing, deployment, or support experience.

Role purpose

Build complete production-grade agentic solutions, not only the agent layer. The engineer will work from business-process understanding through implementation, integration, testing, deployment, monitoring, and continuous improvement. Each engineer must be able to develop the surrounding application and workflow components required to make an agent useful, secure, reliable, and operable.

Client and delivery context

•   Three engineers will form the main implementation capacity of the SWAT pod and may be split across parallel business-process tracks.

•   The client already has platform, security, governance, and architecture capabilities; engineers are expected to use those services and focus on delivery.

•   Assignments may require agent logic, conventional backend development, data processing, integration, UI development, or workflow automation depending on the use case.

•   Candidates must be comfortable learning unfamiliar tools and working across languages or frameworks when that is the best fit for the client platform.

Primary ownership

•   End-to-end implementation of agents, services, workflows, retrieval, integrations, user interfaces, tests, and operational instrumentation.

•   High-quality production code that follows existing enterprise security, architecture, CI/CD, and observability standards.

•   Structured evaluation, debugging, performance improvement, and production support for assigned use cases.

•   Reusable modules, tool patterns, and implementation documentation for broader adoption.

Key responsibilities

•   Analyze process requirements and translate them into technical stories, interfaces, agent behaviors, workflow steps, validation rules, and acceptance criteria.

•   Build agents using appropriate patterns for tool use, planning, retrieval, state, memory, structured outputs, approvals, and exception handling.

•   Develop backend services, APIs, microservices, event handlers, data transformations, schedulers, and deterministic workflow components that support the agent.

•   Build or integrate user-facing experiences using web, chat, assistant, or embedded application patterns where required.

•   Integrate agents with enterprise systems, knowledge bases, data stores, search services, identity platforms, and approved model endpoints.

•   Implement RAG and context-engineering patterns, including ingestion, chunking, metadata, retrieval, reranking, grounding, citations, and access-aware filtering.

•   Write unit, integration, contract, security, and end-to-end tests; contribute to golden datasets, simulation, adversarial testing, and regression suites.

•   Instrument applications with logs, traces, metrics, token and cost telemetry, tool-call visibility, and user-feedback capture.

•   Deploy through existing CI/CD pipelines and support configuration, environment promotion, release validation, rollback, incident diagnosis, and remediation.

•   Participate in design and code reviews, document implementation decisions, and transfer reusable patterns to other engineers.

Must-have candidate profile

•   7+ years of professional software, AI/ML, platform, or application-engineering experience with strong hands-on development skills.

•   Production experience building LLM, RAG, AI assistant, agentic, intelligent workflow, or AI-enabled application capabilities.

•   Strong proficiency in Python, Java, TypeScript/Node.js, or a comparable enterprise language; willingness to work across the stack.

•   Solid knowledge of APIs, microservices, distributed systems, asynchronous processing, data structures, error handling, and automated testing.

•   Practical understanding of prompts, tool calling, structured outputs, context windows, embeddings, retrieval, state, memory, and agent failure modes.

•   Experience with containers, cloud services, CI/CD pipelines, source control, secrets, environment configuration, and application monitoring.

•   Ability to debug complex failures across model behavior, prompts, tools, data, network calls, application code, and infrastructure.

•   Clear communication, ownership mindset, and ability to deliver in a fast-moving, highly technical client environment.

Preferred experience

•   Experience developing MCP tools or integrating with MCP servers.

•   Full-stack experience with modern web frameworks such as React, Angular, or equivalent.

•   Experience with vector databases, enterprise search, graph databases, document processing, or multimodal data.

•   Experience with one or more agent frameworks while retaining strong framework-independent engineering fundamentals.

•   Experience with model evaluation, AI tracing, prompt and tool versioning, red-team testing, and production quality gates.

•   Financial services, enterprise productivity, IT operations, identity, service management, or data-platform integration experience.

Indicative technology exposure

Python, Java, TypeScript/Node.js; FastAPI, Flask, Spring Boot, Express/NestJS; React or comparable UI frameworks; LangGraph, Semantic Kernel, AutoGen, LlamaIndex, LangChain, CrewAI, or equivalent; MCP; REST/gRPC/events; SQL/NoSQL; enterprise search/vector stores; Docker/Kubernetes/serverless; Git-based CI/CD; tracing and evaluation platforms. Tool equivalence is acceptable.

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form,  .

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