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.
• 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.
• 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.