AI engineer, Advisor

About the Role

NTT DATA is seeking an AI engineer - Advisor to join their team.

Key Responsibilities

• 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.

Requirements & Tech Stack

• 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.