{"id":2671431,"url":"https://alion.io/job/ntt-data-ai-engineers-advisor","title":"AI engineers - Advisor","company":{"id":50348,"name":"NTT DATA","domain":"nttdata.com","url":"https://alion.io/company/ntt-data","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Phenom","truth_index":{"grade":"B","score":75,"open_postings":26,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":25,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Tool Use","optional":false},{"name":"PostgreSQL","optional":true},{"name":"ServiceNow","optional":true}],"status":"live","first_seen_at":"2026-10-09T08:13:51Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-16T09:39:49Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Req ID: 392017\nNTT 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.\nWe are currently seeking a AI engineers - Advisor to join our team in Bangalore, Karnātaka (IN-KA), India (IN).\nAI & Generative AI Development\nDesign and develop AI and Generative AI applications using Large Language Models (LLMs) and modern AI frameworks.\nBuild and integrate AI agents and agentic workflows capable of reasoning, tool calling, task execution, and interaction with enterprise systems.\nDevelop prompt engineering, context management, structured-output, and function/tool-calling capabilities.\nImplement Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, embeddings, semantic search, and vector databases.\nDevelop reusable AI components, APIs, services, connectors, and accelerators.\nIntegrate AI capabilities into web applications, enterprise platforms, workflows, and existing business systems.\nLLM & Agentic AI Engineering\nIntegrate commercial and open-source LLMs using APIs and model-serving platforms.\nDevelop single-agent and multi-agent workflows using appropriate orchestration frameworks.\nImplement agent tools, memory, state management, workflow orchestration, and human-in-the-loop capabilities.\nImplement mechanisms to improve grounding and reduce hallucinations and unreliable responses.\nDevelop AI guardrails, input/output validation, content controls, and error-handling mechanisms.\nOptimize prompts, model parameters, retrieval strategies, and workflows for quality, latency, reliability, and cost.\nRAG & Knowledge Engineering\nBuild ingestion and indexing pipelines for structured and unstructured enterprise content.\nImplement document parsing, chunking, metadata enrichment, embedding generation, indexing, and retrieval.\nDevelop semantic, keyword, and hybrid search capabilities.\nIntegrate vector databases and enterprise search platforms.\nOptimize retrieval quality through query transformation, reranking, filtering, and contextual retrieval techniques.\nImplement source attribution and grounding mechanisms where required.\nIntegration & API Development\nDevelop REST APIs, microservices, event-driven integrations, and backend services supporting AI applications.\nIntegrate AI solutions with enterprise applications, databases, APIs, SaaS platforms, and external services.\nImplement authentication, authorization, secrets management, and secure API communication.\nDevelop connectors and tools that enable AI agents to securely interact with enterprise systems.\nTesting & AI Evaluation\nDevelop automated tests for AI applications, APIs, workflows, and integrations.\nBuild evaluation datasets and test scenarios for LLM and RAG solutions.\nEvaluate AI outputs for accuracy, relevance, groundedness, completeness, safety, and consistency.\nPerform prompt, model, retrieval, and agent workflow evaluations.\nTroubleshoot AI application issues and perform root-cause analysis.\nSupport performance, scalability, resilience, and security testing.\nDeployment & LLMOps/MLOps\nDeploy AI applications and services into development, test, staging, and production environments.\nImplement CI/CD pipelines for AI applications and supporting services.\nContainerize applications using Docker and deploy to Kubernetes, serverless, or cloud-native environments.\nImplement model, prompt, configuration, and application version management.\nSupport LLMOps/MLOps processes including deployment, monitoring, evaluation, and lifecycle management.\nImplement logging, tracing, metrics, token/cost monitoring, and AI observability.\nSecurity & Responsible AI\nImplement security controls defined by solution architecture and organizational standards.\nProtect sensitive and personally identifiable information used by AI applications.\nImplement safeguards against prompt injection, data leakage, unauthorized tool execution, and inappropriate AI outputs.\nSupport Responsible AI requirements including transparency, traceability, auditability, and human oversight.\nTechnical Collaboration\nWork closely with AI Technical Architects to implement approved solution architectures and design patterns.\nParticipate in technical design sessions, code reviews, architecture reviews, and troubleshooting sessions.\nCollaborate with data, application, cloud, cybersecurity, and infrastructure teams.\nProduce technical documentation covering application design, APIs, integrations, deployment, configuration, and operational procedures.\nIdentify technical risks, dependencies, and implementation constraints and escalate them appropriately.\nRequired Technical Skills\nStrong programming skills in Python and experience developing production-grade applications.\nHands-on experience with Generative AI, LLMs, RAG, and Agentic AI.\nExperience integrating LLM platforms such as OpenAI/Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, or equivalent technologies.\nExperience with AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.\nExperience with embeddings, semantic search, and vector databases such as Azure AI Search, Pinecone, Elasticsearch/OpenSearch, pgvector, FAISS, Milvus, or equivalent.\nStrong understanding of REST APIs, JSON, microservices, and enterprise integration patterns.\nExperience with SQL and NoSQL databases.\nExperience with Git and modern software-development practices.\nKnowledge of Docker, Kubernetes, CI/CD, DevOps, and cloud-native development.\nExperience working with Azure, AWS, and/or Google Cloud Platform.\nUnderstanding of authentication, authorization, API security, encryption, and secrets management.\nPreferred Skills\nExperience developing enterprise AI agents and multi-agent solutions.\nExperience building production-grade RAG and enterprise knowledge solutions.\nExperience with conversational AI, virtual assistants, or voice-based AI solutions.\nExperience integrating AI with platforms such as ServiceNow, Salesforce, SAP, Microsoft 365, contact-center platforms, or other enterprise applications.\nExperience with AI evaluation and observability frameworks.\nFamiliarity with MLOps/LLMOps practices and model lifecycle management.\nUnderstanding of Responsible AI, AI governance, and enterprise security requirements.\nAbout NTT DATA\nNTT 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.\nWhenever 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.\nNTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. 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