JOB SUMMARY We are seeking a skilled and motivated AI Engineer (Mid-Level) to design, build, and deploy AI-powered solutions supporting P&C insurance operations. This role focuses on Generative AI, MLOps, and Intelligent Agent development, requiring close collaboration with 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 position requiring comfort moving from architecture to working code. Hybrid work - local candidates only. Key Responsibilities: Design, fine-tune, and deploy Large Language Models (LLMs) for insurance-specific use cases. Build Retrieval-Augmented Generation (RAG) pipelines using vector databases. Develop prompt engineering frameworks and systematic evaluation pipelines for LLM output quality and safety. Integrate LLM capabilities with internal data platforms via LangChain, LlamaIndex, or Semantic Kernel. Evaluate and benchmark foundational models against insurance-specific tasks. Architect and implement autonomous AI agents 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. Design human-in-the-loop (HITL) checkpoints and escalation logic for AI agents. Integrate agents with internal APIs, data platforms, and enterprise systems using orchestration tools. Develop guardrails, monitoring, and audit logging for all deployed agents. Build and maintain end-to-end MLOps pipelines covering model training, versioning, validation, deployment, and monitoring. Implement CI/CD pipelines for ML models. Deploy models as REST APIs or batch inference services, 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. Collaborate with data engineering teams to ensure feature pipelines are production-grade, versioned, and integrated with the Feature Store. Work closely with business analysts, actuaries, underwriters, and claims professionals to translate domain requirements into AI solution designs. Participate in Agile/Scrum ceremonies. Produce clear, well-structured technical documentation. Mentor junior engineers and contribute to internal AI engineering best practices and standards. Required Qualifications: Bachelor's degree in Computer Science, Data Science, Machine Learning, Software Engineering, or a related quantitative field. 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. Proficiency in 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 Proficiency in 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 Proficiency in 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 Proficiency in 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 Proficiency in 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 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. Exposure to Data Mesh patterns and publishing AI model outputs as domain data products. Familiarity with Databricks Model Serving and Mosaic AI capabilities. Certifications: Microsoft certifications: Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) preferred. Education: Bachelors Degree Certification: Microsoft certifications: Azure AI Engineer Associate (AI-102) , Azure Data Scientist Associate (DP-100)
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: compun
- Position Id: KUMDC5784900
- Posted 2 hours ago
Never miss an opportunity! Create an alert based on the job you applied for.
