Looking for AI Engineer in Warren, NJ - 4 days onsite - F2F Interview Required

AI Engineer

Location: Warren, NJ - 4 days onsite  

Duration: Long Term Contract

 

In-person Interviews – Need locals

 

Position Overview

 

We are seeking a skilled and motivated AI Engineer (Mid-Level) to join on a contract basis. 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

< style="margin: 0cm; margin-bottom: .0001pt;">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.

< style="margin: 0cm; margin-bottom: .0001pt;">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.

< style="margin: 0cm; margin-bottom: .0001pt;">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.

< style="margin: 0cm; margin-bottom: .0001pt;">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.

 

< style="margin: 0cm; margin-bottom: .0001pt;">Required Qualifications< style="margin: 0cm; margin-bottom: .0001pt;"> 

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.

< style="margin: 0cm; margin-bottom: .0001pt;"> < style="margin: 0cm; margin-bottom: .0001pt;">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)