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.

Tools, Platforms & Engineering Environment

This role will work with the AI engineering stack PURE is actively building and scaling, including Claude Code, OpenAI Codex, GitHub Copilot, AWS ECS, AWS AgentCore Gateway, AWS AgentCore Harness, and Databricks.

The engineer will help build and deploy production AI applications as containerized services, including AI agents, copilots, knowledge assistants, RAG-based applications, and model-powered workflows. This includes integrating LLMs with enterprise tools and data sources, defining reusable agent skills and tool interfaces, building governed knowledge bases, and deploying AI workloads with strong security, observability, and cost controls.

Strong candidates will have hands-on experience with Python, APIs, containerized applications, cloud-native deployment patterns, LLM application development, agent orchestration, RAG, evaluation frameworks, and modern AI-assisted engineering tools. Experience with LangChain, LangGraph, open-source model fine-tuning or adaptation, and Databricks-based AI/ML workflows is highly valuable.

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 architecture reviews, decision documentation (ADRs), and engineering standards that the broader team can build on.

Collaboration & Impact

• Work across Engineering, Product, Data Science, Compliance, and business operations to translate member and business needs into AI-powered solutions.
• Mentor engineers on the team; elevate technical quality through code reviews, pairing, and knowledge sharing.
• Communicate clearly with both technical and non-technical stakeholders — able to explain an LLM tradeoff to an underwriter or a latency constraint to a product manager.
• Stay current on the rapidly evolving AI landscape and bring actionable signal — new models, frameworks, patterns — back to the team.

• 7+ years of professional software engineering experience, with at least 3 years building and operating AI/ML systems in production.
• Proven hands-on experience with LLMs: prompt engineering, RAG pipelines, fine-tuning or adapting open-source models, function/tool calling, agent orchestration, and working with Claude, OpenAI/Codex, Gemini, or comparable models via API..
• Experience building and shipping agentic AI systems, multi-step agents, tool-use orchestration, reusable agent skills, autonomous workflow automation, and governed enterprise integrations in a production environment. Experience with AWS AgentCore Gateway, AWS AgentCore Harness, LangChain, LangGraph, or comparable agent frameworks is highly valuable..
• Strong Python engineering skills; ability to write clean, maintainable, production-grade code with FastAPI or similar frameworks, and package AI capabilities as APIs, services, workers, or containerized applications..
• Experience with AI/ML infrastructure: Databricks-based AI/ML workflows, knowledge bases, vector or hybrid search, feature pipelines, model serving patterns, container orchestration, Docker, and cloud-native deployment
• Familiarity with cloud-native AI workloads on AWS — including cost governance and performance tuning at scale.
• Experience implementing trust, safety, and governance controls in AI systems: PII handling, content filtering, access controls, and auditability.
• Comfort working in a delivery-oriented team: you ship, you measure, you iterate.
• Hands-on experience with AI-assisted software engineering tools such as Claude Code, OpenAI Codex, GitHub Copilot, or comparable developer productivity platforms.
• Experience creating reusable AI agent artifacts such as skill files, tool definitions, prompt templates, system instructions, evaluation datasets, and guardrail patterns.

Preferred Qualifications:

• Background in insurance, fintech, or other regulated industries, with familiarity with compliance frameworks such as SOC 2, NAIC model laws, or GDPR.
• Experience with LLM observability tooling: LangSmith, Weights & Biases, Dynatrace LLM monitoring, OpenTelemetry, or equivalent.
• Familiarity with ML frameworks (PyTorch, scikit-learn) for classical ML alongside LLM-based approaches.
• Experience with fraud detection, document intelligence, risk scoring, or actuarial data systems.
• Contributions to open-source AI/ML projects or published work in applied NLP or machine learning.
• Experience deploying AI applications as containerized services on AWS ECS or similar cloud-native platforms.
• Experience designing and implementing agent skills, tool registries, function-calling interfaces, or reusable agent capabilities for enterprise AI systems.

• LLM-powered products shipped to production with measurable impact on member experience and operational efficiency.
• AI systems that are reliable, governed, and trusted by compliance, risk, and operations teams — not just engineering.
• Robust observability across all AI pipelines: cost is tracked, latency is within SLO, and nothing breaks silently.
• A stronger AI engineering culture: the team learns from your code reviews, your ADRs, and your engineering standards.
• Faster, higher-quality underwriting, claims, and member service decisions enabled by production AI.

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To ensure a successful onboarding experience, all new hires must work onsite at one of our offices during their first week of employment. Candidates should apply only if they are able to meet this requirement.

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