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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** NTT DATA, Inc. - **Location:** Warren, NJ, United States (Remote available) - **Salary:** $156,000.0 - $166,400.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Audit Trail, Microsoft Azure, Big Data, Cloud Computing, Computer Programming, Continuous Integration, Data Validation, Information Engineering, Github, Monitoring of Systems, Apache Hive, Python (Programming Language), Key Management, Machine Learning, NumPy, Open Source Technology, Scrum Methodology, Azure Machine Learning, Search Technologies, Software Engineering, SQL Databases, Transact-SQL, Management of Software Versions, Workflow Management Systems, Enterprise Software Applications, Cloud Platform System, Feature Engineering, Azure Data Factory, Pytorch, ReactJS, Large Language Models, Prompt Engineering, Generative AI, Git, Pandas, Microsoft Fabric, Data Lakes, Pyspark, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Azure AKS, Graphql, Data Management, Machine Learning Operations, Api Design, Restful APIs, Webhooks, GPT, Docker, Databricks - **Published:** June 3, 2026 - **Apply:** https://dejobs.org/x/x/E225AE43FA654854915C35363DCC9741/job/ ## About the Role Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. Master's degree is a plus. Experience 35 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 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. ## Description 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. 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