AI engineer - Advisor

Req ID: 392020 

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 AI engineer - Advisor to join our team in Bangalore, Karnātaka (IN-KA), India (IN).

AI & Generative AI Development

• Design and develop AI and Generative AI applications using Large Language Models (LLMs) and modern AI frameworks.
• Build and integrate AI agents and agentic workflows capable of reasoning, tool calling, task execution, and interaction with enterprise systems.
• Develop prompt engineering, context management, structured-output, and function/tool-calling capabilities.
• Implement Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, embeddings, semantic search, and vector databases.
• Develop reusable AI components, APIs, services, connectors, and accelerators.
• Integrate AI capabilities into web applications, enterprise platforms, workflows, and existing business systems.

LLM & Agentic AI Engineering

• Integrate commercial and open-source LLMs using APIs and model-serving platforms.
• Develop single-agent and multi-agent workflows using appropriate orchestration frameworks.
• Implement agent tools, memory, state management, workflow orchestration, and human-in-the-loop capabilities.
• Implement mechanisms to improve grounding and reduce hallucinations and unreliable responses.
• Develop AI guardrails, input/output validation, content controls, and error-handling mechanisms.
• Optimize prompts, model parameters, retrieval strategies, and workflows for quality, latency, reliability, and cost.

RAG & Knowledge Engineering

• Build ingestion and indexing pipelines for structured and unstructured enterprise content.
• Implement document parsing, chunking, metadata enrichment, embedding generation, indexing, and retrieval.
• Develop semantic, keyword, and hybrid search capabilities.
• Integrate vector databases and enterprise search platforms.
• Optimize retrieval quality through query transformation, reranking, filtering, and contextual retrieval techniques.
• Implement source attribution and grounding mechanisms where required.

Integration & API Development

• Develop REST APIs, microservices, event-driven integrations, and backend services supporting AI applications.
• Integrate AI solutions with enterprise applications, databases, APIs, SaaS platforms, and external services.
• Implement authentication, authorization, secrets management, and secure API communication.
• Develop connectors and tools that enable AI agents to securely interact with enterprise systems.

Testing & AI Evaluation

• Develop automated tests for AI applications, APIs, workflows, and integrations.
• Build evaluation datasets and test scenarios for LLM and RAG solutions.
• Evaluate AI outputs for accuracy, relevance, groundedness, completeness, safety, and consistency.
• Perform prompt, model, retrieval, and agent workflow evaluations.
• Troubleshoot AI application issues and perform root-cause analysis.
• Support performance, scalability, resilience, and security testing.

Deployment & LLMOps/MLOps

• Deploy AI applications and services into development, test, staging, and production environments.
• Implement CI/CD pipelines for AI applications and supporting services.
• Containerize applications using Docker and deploy to Kubernetes, serverless, or cloud-native environments.
• Implement model, prompt, configuration, and application version management.
• Support LLMOps/MLOps processes including deployment, monitoring, evaluation, and lifecycle management.
• Implement logging, tracing, metrics, token/cost monitoring, and AI observability.

Security & Responsible AI

• Implement security controls defined by solution architecture and organizational standards.
• Protect sensitive and personally identifiable information used by AI applications.
• Implement safeguards against prompt injection, data leakage, unauthorized tool execution, and inappropriate AI outputs.
• Support Responsible AI requirements including transparency, traceability, auditability, and human oversight.

Technical Collaboration

• Work closely with AI Technical Architects to implement approved solution architectures and design patterns.
• Participate in technical design sessions, code reviews, architecture reviews, and troubleshooting sessions.
• Collaborate with data, application, cloud, cybersecurity, and infrastructure teams.
• Produce technical documentation covering application design, APIs, integrations, deployment, configuration, and operational procedures.
• Identify technical risks, dependencies, and implementation constraints and escalate them appropriately.

Required Technical Skills

• Strong programming skills in Python and experience developing production-grade applications.
• Hands-on experience with Generative AI, LLMs, RAG, and Agentic AI.
• Experience integrating LLM platforms such as OpenAI/Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, or equivalent technologies.
• Experience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.
• Experience with embeddings, semantic search, and vector databases such as Azure AI Search, Pinecone, Elasticsearch/OpenSearch, pgvector, FAISS, Milvus, or equivalent.
• Strong understanding of REST APIs, JSON, microservices, and enterprise integration patterns.
• Experience with SQL and NoSQL databases.
• Experience with Git and modern software-development practices.
• Knowledge of Docker, Kubernetes, CI/CD, DevOps, and cloud-native development.
• Experience working with Azure, AWS, and/or Google Cloud Platform.
• Understanding of authentication, authorization, API security, encryption, and secrets management.

Preferred Skills

• Experience developing enterprise AI agents and multi-agent solutions.
• Experience building production-grade RAG and enterprise knowledge solutions.
• Experience with conversational AI, virtual assistants, or voice-based AI solutions.
• Experience integrating AI with platforms such as ServiceNow, Salesforce, SAP, Microsoft 365, contact-center platforms, or other enterprise applications.
• Experience with AI evaluation and observability frameworks.
• Familiarity with MLOps/LLMOps practices and model lifecycle management.
• Understanding of Responsible AI, AI governance, and enterprise security requirements.

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

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, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.

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