AI Engineer (Generative AI MLOps AI Agents)

Role: AI Engineer (Generative AI / MLOps / AI Agents)

Location: Warren NJ (Hybrid)

Type: Contract

Overview:

• We are seeking a skilled and motivated AI Engineer (Mid-Level) to join Client USA 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:

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:

• Bachelors degree in Computer Science Data Science Machine Learning Software Engineering or a related quantitative field. Masters 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 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.