NTT DATA is seeking an innovative AI Engineer – Advisor to join our dynamic team in Bangalore, India. This role focuses on designing and developing cutting-edge AI and Generative AI applications, leveraging Large Language Models (LLMs) and modern AI frameworks. You will play a crucial role in building intelligent agents and workflows that can reason, execute tasks, and interact seamlessly with enterprise systems, driving forward our commitment to responsible innovation and accelerating client success.

Key responsibilities include developing and integrating AI agents and agentic workflows, mastering prompt engineering, and implementing Retrieval-Augmented Generation (RAG) solutions. You will also develop reusable AI components, APIs, and services, integrating AI capabilities into various enterprise platforms and business systems. This includes integrating commercial and open-source LLMs, developing single and multi-agent workflows, and optimizing prompts and model parameters for enhanced quality and performance. Additionally, you'll build RAG and knowledge engineering pipelines, develop semantic search capabilities, and integrate vector databases. Responsibilities extend to developing REST APIs, microservices, and backend services for AI applications, ensuring secure integration with enterprise systems. You will also be responsible for automated testing of AI applications, evaluating AI outputs for accuracy and safety, and supporting LLMOps/MLOps processes for deployment and monitoring. Ensuring security and Responsible AI principles are implemented is also a core part of this role, including safeguarding against common AI vulnerabilities and supporting transparency and human oversight.

We are looking for candidates with strong Python programming skills and proven experience developing production-grade applications. Essential hands-on experience includes Generative AI, LLMs, RAG, and Agentic AI. Proficiency in integrating LLM platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock) and AI frameworks such as LangChain or LangGraph is required. You should have experience with embeddings, semantic search, and vector databases like Pinecone or FAISS. A solid understanding of REST APIs, JSON, microservices, enterprise integration patterns, SQL, and NoSQL databases is necessary. Familiarity with Git, Docker, Kubernetes, CI/CD, DevOps, cloud-native development, and major cloud platforms (Azure, AWS, GCP) is crucial. Knowledge of authentication, authorization, API security, and secrets management is also a requirement. Preferred skills include experience developing enterprise AI agents, production-grade RAG solutions, and integrating AI with enterprise applications like ServiceNow or Salesforce. Experience with AI evaluation frameworks, MLOps/LLMOps, and Responsible AI principles is highly advantageous.