AI Engineer
Posted 14 Oct 2026
Advertised as “AI engineer - Advisor”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
Compensation
No salary listed for this role.
Role speciality
Agentic AIAI EngineeringArtificial IntelligenceGenerative AILarge Language ModelsLLMOpsMLOpsRetrieval-Augmented Generation
Useful technologies
AnthropicAutoGenAWSAWS BedrockAzureAzure AI SearchAzure OpenAIDockerElasticsearchFAISSGitGoogle Cloud PlatformGoogle GeminiKubernetesLangChainLangGraphMicrosoft 365MilvusOpenAIOpenSearchpgvectorPineconePythonSalesforceSAPSemantic KernelServiceNowSQL
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.