AI Engineer (Generative AI / MLOps / AI Agents)

NTT DATA, Inc.; Hyderabad; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

NTT DATA in Hyderabad, Telangana, is seeking a mid‑level AI Engineer to design, build, and deploy Generative AI, MLOps, and AI agent solutions for insurance operations. You will collaborate with data engineering and business teams to deliver LLM‑powered applications, automated agents, and production‑ready ML pipelines across claims, underwriting, and actuarial domains.

This hands‑on role requires transforming architecture whiteboards into production code and scalable services, with strong

Qualifications

• 3–5 years of professional experience in AI/ML engineering, with production-grade AI systems.
• Hands-on experience building and deploying LLM-powered applications using LangChain, LlamaIndex, or Semantic Kernel.
• Proven experience implementing MLOps pipelines in cloud environments (Azure preferred).
• Experience developing AI agents or automation workflows using agentic frameworks.
• Prior experience in financial services, insurance, or regulated industries is strongly preferred.

Responsibilities

• Design, build, and deploy AI-powered solutions for P&C insurance operations.
• Deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines.
• Create AI agent frameworks with HITL checkpoints and escalation logic.
• Integrate agents with internal data platforms and enterprise systems.
• Establish robust MLops pipelines and model monitoring.

Skills

AI/ML engineering

Production-grade AI systems

Deep learning

Education

Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or related field

Tools

LangChain

LlamaIndex

Semantic Kernel

Azure ML

MLflow

Job description

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,

We are currently seeking a AI Engineer (Generative AI / MLOps / AI Agents) to join our team in Hyderabad, Telangana (IN-TG), India (IN).

We are seeking a skilled and motivatedAI Engineer(Mid-Level) to join contract basis. This role sits at the intersection ofGenerative AI, MLOps, and Intelligent Agent development— and is responsible for designing, building, and deploying AI-powered solutions that directly support our P&C insurance operations.

You will work closely with our data engineering, analytics, and business teams to deliverLLM-powered applications, automated AI agents, and production-ready ML pipelinesacross claims, underwriting, and actuarial domains. This is a hands‑on, delivery-focused role for an engineer who is comfortable moving from architecture whiteboard to working code.

Key Responsibilities

• Design, fine‑tune, and deployLarge Language Models (LLMs)for insurance‑specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting Q&A.
• BuildRetrieval‑Augmented Generation (RAG)pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.
• Developprompt engineering frameworksand systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts.
• Integrate LLM capabilities with internal data platforms viaLangChain, LlamaIndex, or Semantic Kernel.
• Evaluate and benchmark foundational models (OpenAI GPT‑4o, Azure OpenAI, Claude, Mistral, Llama) against insurance‑specific tasks to guide platform selection.

AI Agents & Automation

• Architect and implementautonomous AI agentscapable of multi‑step reasoning, tool use, and decision‑making for workflows such as FNOL triage, claims routing, policy lookup, and compliance checks.
• Build agentic frameworks using patterns such asReAct, Chain‑of‑Thought, and Tool‑Augmented Agentsto handle complex, multi‑turn insurance workflows.
• Designhuman‑in‑the‑loop (HITL)checkpoints and escalation logic to ensure AI agents operate within defined risk and compliance boundaries.
• Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools such asAzure Logic Apps, Apache Airflow, or Databricks Workflows.
• Develop guardrails, monitoring, and audit logging for all deployed agents to meet regulatory and governance standards.
• Build and maintainend‑to‑end MLOps pipelinescovering model training, versioning, validation, deployment, and monitoring usingMLflow, Azure ML, and Databricks.
• ImplementCI/CD pipelines for ML modelsusing Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.
• Deploy models asREST APIs or batch inference serviceson Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low‑latency response.
• Establishmodel monitoring frameworksto detect data drift, model degradation, and prediction anomalies in production.
• Manage themodel registry and lineage trackingto maintain governance and auditability of all AI assets.
• Collaborate with data engineering teams to ensure feature pipelines are production‑grade, versioned, and integrated with theFeature Storeon Databricks or Azure ML.
• Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs.
• Participate inAgile/Scrum ceremoniesincluding sprint planning, standups, and retrospectives as an active delivery contributor.
• Produce clear, well‑structuredtechnical documentationincluding solution designs, API specs, model cards, and deployment runbooks.
• Mentor junior engineers and contribute to internal AI engineering best practices and standards.

Required Qualifications

Education

• Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master's degree is a plus.

Experience

• 3–5 years of professional experience in AI/ML engineering, with demonstrated delivery ofproduction‑grade AI systems.
• Hands‑on experience building and deployingLLM‑powered applicationsusing frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Proven experience implementingMLOps pipelinesin cloud environments (Azure preferred).
• Experience developingAI agents or automation workflowsusing agentic frameworks.
• Prior experience infinancial services, insurance, or regulated industriesis strongly preferred.

Technical Skills

Generative AI & LLMs

• RAG architectures, vector search, embeddings (OpenAI, Cohere, SentenceTransformers)
• LangChain, LlamaIndex, Semantic Kernel
• Prompt engineering, few‑shot learning, instruction tuning, RLHF concepts

AI Agents & Automation

• Agentic frameworks: ReAct, Tool‑Augmented Agents, LangGraph, AutoGen, CrewAI
• Workflow orchestration: Apache Airflow, Databricks Workflows, Azure Logic Apps
• API design and integration: REST, GraphQL, Webhooks

MLOps & Model Serving

• Model monitoring: Evidently AI, Azure ML monitoring, or equivalent
• Python (expert level): PyTorch, Hugging Face Transformers, scikit‑learn, Pandas, NumPy
• PySpark and Delta Lake for large‑scale data processing
• SQL (T‑SQL / Spark SQL) for feature engineering and data validation
• Git for version control and collaborative development
• Microsoft Fabric / OneLake (familiarity a strong plus)

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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