AI Engineer

AI Engineer

Onsite in Warren New Jersey Contract Must be able to work on NTT DATA payroll. No C2C available at this time.

Company Overview: 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 an AI Engineer to join our team in Warren, New Jersey (US-NJ), United States (US).

Job Description

Position Overview 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 P&C 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 Q&A. 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.

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 P&C 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 trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future.

This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on several factors, including the candidate's actual work location, relevant experience, technical skills, and other qualifications. This position is eligible for company benefits that will depend on the nature of the role offered. Company benefits may include medical, dental, and vision insurance, flexible spending or health savings account, life, and AD&D insurance, short-and long-term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally required benefits.