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About This Role
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.
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.
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 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.
What we are looking for:
- 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.
What Success Looks Like:
- 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.
What We Do
We're a member\-owned property and casualty insurer designed exclusively for financially successful families and driven by a purpose of doing what is right for our members. We provide exceptional service, hospitality and care, we partner with our members to help prevent losses and we create smart insurance solutions at fair prices.
We aim for our members to *love their insurance* . It is our mission is to create a membership experience so compelling that our members never want to leave.
Who We Are
We want to be transparent about what we expect from each other. From PURE, you can expect:
*Opportunities to stretch and grow:* your professional and personal development matters to us. We’re committed to providing experiences through on\-the\-job learning and professional development that increase your impact and rewards.
*Clarity and kindness:* you can rely on us to be open, honest and supportive, offering clarity on what success looks like.
*Support in good times and bad:* we believe in showing up for each other consistently, not only when it’s easy. You can expect a thoughtful partner, even when we disagree.
*A community that cares:* we are committed to sustaining a community in which each person feels cared for as an individual. We lift each other up, celebrate wins together and support one another through challenges in work and life.
Who You Are
All of the strongest relationships are a partnership\- a two way street. So here’s what we ask of you:
- Aim to bring your best every day: you’re here because you want to be part of a team that makes a real impact and aims high.
- Be a student and a teacher: share your knowledge and talents and be willing to listen and learn from those around you.
- Get comfortable being uncomfortable: we face tough moments and obstacles with a “courage over comfort” approach and a positive, solutions\-oriented mindset.
- Be a culture builder: building a positive culture is everyone's responsibility, based on care, respect and openness to diverse perspectives.
The base salary for this role can range from $155,000 to $180,000 based on a full\-time work schedule. An individual’s ultimate compensation will vary depending on job\-related skills and experience, geographic location, alignment with market data, and equity among other team members with comparable experience.
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.
Salary Context
This $155K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PURE Insurance, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($167K) sits 23% below the category median. Disclosed range: $155K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PURE Insurance AI Hiring
PURE Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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