Senior AI Engineer

About the role: Join our AI & Engineering team in transforming technology platforms, driving innovation, and making a significant impact on our members’ success. You will work alongside talented professionals reimagining and re-engineering operations and processes that are critical to our business — from underwriting and claims to member experience and risk management.

Your contributions will help PURE improve operational performance, accelerate new digital capabilities, and fuel growth through innovation. Our AI & Engineering practice leverages cutting-edge engineering to build, deploy, and operate integrated solutions across software, data, AI, and cloud infrastructure — all in service of members who expect more from their insurance company.

This role is hands-on and delivery-oriented. You will ship production pipelines, APIs, agents, and containerized services that support model training, real-time inference, RAG, and LLM-powered applications using Claude Code, OpenAI Codex, GitHub Copilot, AWS ECS, AWS AgentCore Gateway, AWS AgentCore Harness, and Databricks. You will help turn AI concepts into governed, observable, secure, and cost-effective production systems. You will work in close partnership with the Lead AI Solutions Architect and AI Data Engineer to bring AI-powered products from design to production.

What you’ll do: Build & Deploy AI Solutions

• Partner with the Lead AI Solutions Architect and AI Data Engineer to design, build, and deploy secure, scalable AI solutions: APIs, services, pipelines, agents, containers, and serverless functions that meet availability, performance, and security requirements. Deploy AI workloads primarily using cloud-native patterns, including AWS ECS-based containerized applications.
• Build and operationalize LLM-enabled products including copilots, knowledge assistants, summarization engines, policy Q&A tools, and agentic workflows using Claude Code, OpenAI Codex, GitHub Copilot, AWS AgentCore Gateway, AWS AgentCore Harness, Databricks, and comparable LLM platforms. Apply thoughtful prompt and context patterns, tool/function calling, reusable agent skills, and agentic orchestration patterns..
• Implement RAG, knowledge base, and document intelligence patterns end-to-end: ingestion, chunking, embeddings, vector and hybrid search, retrieval evaluation, and telemetry. Build and maintain Databricks-backed knowledge bases and AI agent capabilities where appropriate.
• Deliver governed data and features for ML and GenAI — curated datasets, feature pipelines, and feature serving — supporting both training workflows and real-time inference with consistency, caching, backfill support, and latency SLOs.
• Build reusable AI agent skills, tool definitions, prompts, guardrails, and orchestration patterns that can be shared across PURE’s AI products and engineering teams.
• Use LangChain, LangGraph, or comparable frameworks where appropriate to build agent workflows, tool-use orchestration, stateful reasoning patterns, and multi-step automation.

Governance, Trust & Safety

• Implement trust, safety, and governance controls including PII handling, prompt-injection defenses, content filtering, and policy-based access controls — built in close partnership with security and risk teams.
• Ensure AI outputs are auditable, explainable, and compliant with applicable regulatory requirements (SOC 2, NAIC, GDPR) — a non-negotiable in insurance.
• Define and maintain data lineage and model versioning practices so every production inference can be traced, reproduced, and reviewed.
• Develop evals and red-teaming protocols to proactively identify failure modes in LLM-powered systems before they reach members.

Engineering Excellence & Operations

• Drive CI/CD, testing, versioning, reproducibility, and deployment standards across AI systems, including AWS ECS services, Databricks workflows, LLM applications, RAG pipelines, and agentic workflows.
• Establish and maintain monitoring and observability across the full model lifecycle — from data ingestion through inference — including token/cost telemetry, latency dashboards, and drift detection.
• Own incident response for AI platform issues: triage, root cause analysis, and remediation with appropriate urgency and communication.
• Optimize cost and performance continuously: right-sizing compute, query tuning, caching strategies, and token budget management.
• Support design and deployment readiness through archit…