Data & AI / LLM Engineer

SANS Consulting Services, Inc. is seeking a Senior Data & AI / LLM Engineer to build and operationalize data, machine learning, and Generative AI solutions for insurance-related business needs. The role spans data pipelines and analytics, production ML and LLM applications, and deployment using Azure and DevOps practices.

Responsibilities:

• Design, build, test, and maintain scalable data pipelines and data products using Databricks, Apache Spark/PySpark, Delta Lake, SQL, and Python
• Perform exploratory and advanced data analysis to identify patterns, anomalies, trends, relationships, and opportunities within complex insurance datasets
• Build reliable ingestion, transformation, integration, and data-quality processes across structured and unstructured data
• Work with large-scale datasets across cloud data platforms, APIs, files, databases, and enterprise applications
• Apply sound practices around data quality, lineage, observability, metadata, security, and governance
• Optimize pipelines and workloads for reliability, performance, scalability, and cost
• Develop, evaluate, deploy, and monitor machine learning models using Python and appropriate ML frameworks
• Understand core ML techniques including supervised and unsupervised learning, classification, regression, clustering, feature engineering, model evaluation, and model monitoring
• Translate business problems into analytical or machine-learning approaches and determine when traditional analytics, ML, or Generative AI is most appropriate
• Work with data scientists, business SMEs, and engineering teams to productionize models and analytical solutions
• Design and implement enterprise LLM applications, including Retrieval-Augmented Generation (RAG), document processing, summarization, extraction, classification, conversational assistants, and agentic workflows
• Develop solutions using LLM APIs and frameworks, with experience in areas such as prompt engineering, embeddings, vector search, chunking, grounding, tool/function calling, and structured outputs
• Understand evaluation techniques for LLM solutions, including accuracy, relevance, hallucination risk, latency, cost, security, and responsible AI considerations
• Integrate structured enterprise data and unstructured content into secure and governed AI solutions
• Build reusable AI components and APIs that can be incorporated into broader enterprise applications
• Effectively use modern AI development tools such as GitHub Copilot, Claude, and similar coding assistants to accelerate engineering delivery
• Use AI tools to help create, refactor, debug, test, document, review, and maintain code
• Apply engineering judgment when using AI-generated code, including validating functionality, security, performance, maintainability, and compliance with coding standards
• Demonstrate an AI-first engineering mindset, identifying opportunities to automate repetitive development, analysis, testing, and documentation activities rather than simply using AI as an autocomplete capability
• Build and deploy solutions using the Microsoft Azure technology stack, with relevant experience across services such as Azure Databricks, Azure Storage/Data Lake, Microsoft Entra ID, Key Vault, Azure Functions, APIs, Azure AI services, and related cloud services
• Work within modern CI/CD and DevOps practices using tools such as Azure DevOps and/or GitHub
• Use Git-based source control, branching, pull requests, automated testing, build/release pipelines, infrastructure configuration, and environment management
• Design solutions with appropriate consideration for security, identity, secrets management, logging, monitoring, resilience, scalability, and production support
• Partner with architecture, cybersecurity, data governance, infrastructure, and application teams to ensure solutions meet enterprise technology standards

Requirements:

• Strong professional experience in data engineering, analytics, machine learning, AI, or software engineering
• Advanced programming skills in Python and SQL
• Strong experience with Databricks, Spark/PySpark, and Delta Lake
• Experience designing and supporting production-scale ETL/ELT and data pipelines
• Practical experience performing data analysis on large and complex datasets
• Working knowledge of statistical analysis and machine-learning techniques
• Hands-on experience developing solutions using LLMs and Generative AI
• Experience with RAG, embeddings, vector search, prompt engineering, evaluation, or related LLM patterns
• Experience with the Microsoft Azure cloud ecosystem
• Strong understanding of Git, CI/CD, DevOps, testing, code reviews, and production deployment practices
• Experience using AI coding assistants such as GitHub Copilot, Claude, or comparable tools as part of day-to-day software engineering
• Ability to work independently from an ambiguous business problem through analysis, technical design, implementation, testing, deployment, and production support
• The ideal candidate should have experience working within insurance, financial services, or another highly regulated data-intensive industry, with insurance experience strongly preferred
• Prior experience in P&C, commercial, specialty, or reinsurance environments
• Experience with Azure Databricks and Azure AI services
• Experience operationalizing ML models using MLflow/MLOps practices
• Experience with vector databases/vector search and enterprise RAG architectures
• Experience working with insurance documents and unstructured datasets
• Experience building APIs and microservices supporting data and AI applications
• Familiarity with data governance, data cataloging, lineage, model governance, and Responsible AI practices
• Experience with Infrastructure as Code and automated cloud deployments
• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related discipline, or equivalent professional experience