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Tech Stack
Tools & technologiesAirflowApacheAzureCloudDockerGraphQLKubernetesPythonReactSpark
About the role
Key responsibilities & impact- 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.
- 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.
- 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.
Requirements
What you’ll need- Education: Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related field. An advanced degree is preferred.
- 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.
- Experience with Azure, Databricks and/or Fabric
- Good programming experience on Python and Spark
- 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
- Prior experience in financial services, insurance, or regulated industries is strongly preferred.
Benefits
Comp & perks- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
ATS Keywords
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Hard Skills & Tools
Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)prompt engineeringMLOpsPythonSparkAPI designDockerKubernetesCI/CD