Req ID: 368401
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 SQL and Python Engineers to join our team in Remote, Karnātaka (IN-KA), India (IN).
Job Description
AI Engineer (Generative AI / MLOps / AI Agents)
Location: [City, State] | Hybrid
Employment Type: Contract (6–12 months, with potential for extension)
Position Overview
We are seeking a skilled and motivated AI Engineer (Mid-Level) to join us. This role sits at the intersection of Generative AI, MLOps, and Intelligent Agent development — and is responsible for designing, building, and deploying AI-powered solutions that directly support our PC insurance operations.
You will work closely with our data engineering, analytics, and business teams to deliver LLM-powered applications, automated AI agents, and production-ready ML pipelines across 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
Generative AI LLM Engineering
- Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases including document intelligence, claims summarization, policy interpretation, and underwriting QA.
- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., Azure AI Search, Pinecone, ChromaDB) to ground LLM outputs in enterprise knowledge bases.
- Develop prompt engineering frameworks and systematic evaluation pipelines to ensure LLM output quality, consistency, and safety in regulated insurance contexts.
- Integrate LLM capabilities with internal data platforms via LangChain, 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 implement autonomous AI agents capable 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 as ReAct, Chain-of-Thought, and Tool-Augmented Agents to handle complex, multi-turn insurance workflows.
- Design human-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 as Azure Logic Apps, Apache Airflow, or Databricks Workflows.
- Develop guardrails, monitoring, and audit logging for all deployed agents to meet regulatory and governance standards.
MLOps Model Deployment
- Build and maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring using MLflow, Azure ML, and Databricks.
- Implement CI/CD pipelines for ML models using Azure DevOps or GitHub Actions, enabling reliable, repeatable model releases.
- Deploy models as REST APIs or batch inference services on Azure Kubernetes Service (AKS) or Azure Container Apps, ensuring scalability and low-latency response.
- Establish model monitoring frameworks to detect data drift, model degradation, and prediction anomalies in production.
- Manage the model registry and lineage tracking to maintain governance and auditability of all AI assets.
- Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with the Feature Store on Databricks or Azure ML.
Collaboration Delivery
- Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs.
- Participate in Agile/Scrum ceremonies including sprint planning, standups, and retrospectives as an active delivery contributor.
- Produce clear, well-structured technical documentation including 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 of production-grade AI systems.
- Hands-on experience building and deploying LLM-powered applications using frameworks such as 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.
Technical Skills
Generative AI LLMs
- OpenAI / Azure OpenAI (GPT-4o, GPT-4 Turbo), Claude, Mistral, or open-source LLMs (Llama 3, Falcon)
- 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
- MLflow (experiment tracking, model registry, model serving)
- Azure Machine Learning, Databricks AutoML Feature Store
- Docker, Kubernetes (AKS), Azure Container Apps
- CI/CD: Azure DevOps, GitHub Actions
- Model monitoring: Evidently AI, Azure ML monitoring, or equivalent
Programming Data Engineering
- 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
Cloud Platform
- Microsoft Azure (Azure OpenAI, Azure AI Search, AKS, Azure Data Factory, Azure Key Vault)
- Databricks (Unity Catalog, Delta Live Tables, Workflows)
- Microsoft Fabric / OneLake (familiarity a strong plus)
Preferred Qualifications
- Experience with PC insurance workflows such as FNOL processing, claims triage, underwriting decisioning, or actuarial modeling.
- Familiarity with insurance regulatory requirements including NAIC guidelines and data privacy standards (CCPA, GDPR).
- Experience implementing responsible AI principles — fairness, explainability, and bias mitigation — in regulated environments.
- Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred.
- Exposure to Data Mesh patterns and publishing AI model outputs as domain data products.
- Familiarity with Databricks Model Serving and Mosaic AI capabilities.
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 RD.
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 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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