Applicants must be a resident and work from one of the following states: WA, with occasional travel to our headquarters, located in Seattle, WA.
Who We Are:
At PEMCO, we’re all about people – our customers, our employees, and the community. We’re a mutual insurance company owned by our Northwest policyholders. We provide auto, home, renters, and boat coverage. Recognized by Forbes as one of America’s Best Insurance Companies in both Auto and Home for 2026, based on customer survey feedback, and by Newsweek as one of America’s Greatest Midsize Workplaces 2025. We are consistently recognized for our outstanding customer service, employee expertise, community partnerships, and social impact programs. All of which makes PEMCO a great place to work!
Our social impact programs motivate high achievement by youth in education; build stronger and greener communities; and increase safety at home, on the road, and at play. We’re committed to diversity, equity, inclusion, and belonging, and to fostering an inspiring and inclusive workplace. These efforts create and cultivate an environment that builds fairness and understanding, encourages collaboration and flexibility, and celebrates all the ways in which we’re different and the same – enabling all individuals to achieve their full potential.
Why We Need You:
The Principal AI Platform Engineer role will build and run PEMCO's AI platform: the operational, economic, and governance layer under every AI agent and model the company uses. You own the stack end to end, from GPU compute through model serving, retrieval, and orchestration up to observability and governance, and you are expected to be capable of building and operating it on-premise when the economics justify it, not only consuming managed cloud services. This is a hands-on principal role with organizational influence: you build the systems yourself while setting the standards others build against.
What You’ll Be Doing:
• Own AI economics and observability. Build the consumption telemetry for the entire AI estate (cost per agent, per workflow, per business outcome; token usage; GPU utilization) and the LLM observability layer under it (LangFuse-class tracing, evaluation, drift monitoring). Leadership decisions about AI spend run on your data.
• Run the AI gateway. A single gateway fronting every provider and model (LiteLLM-class or Azure APIM GenAI): routing, fallback chains, quotas, and per-agent cost capture. This is the enforcement point for the economics.
• Stand up open-weight serving. High-throughput serving of open-weight models on Azure GPU capacity (vLLM, Triton/TensorRT-LLM), quantization, right-sizing. You build the sizing evidence that justifies or kills any future on-prem investment.
• Build the retrieval layer. Vector stores, embedding pipelines, and document processing for in-house AI builds, on a governed platform rather than one-off deployments.
• Operate production agents. Deployment, monitoring, incident response, and retirement for the agent fleet (MCP-based orchestration, per-agent least-privilege identity). When an agent supporting a business workflow fails, you own recovery.
• Shape the architecture. Represent platform reliability, security, and economics in AI solution reviews across teams; constructively challenge designs with data and propose alternatives.
• Hold the governance line. RBAC for models and agents, prompt/output guardrails, a seat on the AI Governance Working Group with authority to block deployments that do not meet the bar.
• Compute and acceleration stack: Azure GPU VMs and AKS GPU pools first; on-premise GPU build-out (hardware selection, CUDA stack, Kubernetes GPU scheduling) when the sizing data says so
• Models and serving stack: open-weight model families with high-throughput serving (vLLM, NVIDIA Triton/TensorRT-LLM), quantization, fine-tuning and LoRA adaptation; managed frontier APIs (Azure OpenAI / AI Foundry) as the other half of the portfolio
• Gateway and routing stack: a single AI gateway fronting every provider and model; routing and fallback chains, quotas, per-agent cost capture
• Retrieval and data stack: vector stores, embedding and chunking pipelines, document processing, knowledge-source governance
• Orchestration and agents stack: agent frameworks and protocols (MCP, LangChain/LangGraph, Semantic Kernel), tool registration, per-agent least-privilege identity
• Observability, evaluation, governance stack: LangFuse-class tracing and cost telemetry, evaluation tooling with regression testing before prompt or model changes ship, guardrails, RBAC
• Across all layers: model lifecycle from evaluation to retirement, and cost-against-capability optimization (model selection and routing, caching, batching, token budgets).
What You'll Bring:
• 6+ years in platform engineering, SRE, or technical operations at senior/lead scope
• 2+ years running LLM/AI systems in production (or 4+ years ML platform operations)
• Hands-on open-weight model serving (vLLM, Triton/TensorRT-LLM or equivalent), including quantization and GPU right-sizing
• LLM observability and evaluation experience (LangFuse, LangSmith, Arize class), or the demonstrated ability to stand it up
• Experience building RAG systems: vector stores, embedding pipelines, document processing
• FinOps/cost management for cloud consumption, ideally GPU or AI workloads
• Working knowledge of AI security risks: prompt injection, data leakage, model abuse
• Preferred: on-prem GPU infrastructure design, fine-tuning/LoRA, agent frameworks (MCP, LangChain/LangGraph, Semantic Kernel), regulated-industry experience
Compensation:
The pay range for this role is shown below. Compensation decisions are determined based on an individual’s qualifications, job-related knowledge, skills, and experience.
• Greater Seattle area target pay range: $154,845-$189,255. The full pay range is $129,038-$215,063.
• Outside Greater Seattle area target pay range: $136,654-$167,022. The full pay range is $113,879-$189,797.
Greater Seattle Area is defined as working within approximately 100 miles of Seattle.
Outside Greater Seattle is defined as working approximately 100 miles or more from Seattle.
Benefits:
Regular part-time PEMCO employees working at least 24 hours per week and regular full-time PEMCO employees are eligible to elect coverage under medical, dental, and vision plans for themselves and their eligible family members with generous employer premium cost shares. In addition, as a benefits-eligible employee, you are:
• covered by employer-paid basic life and accidental death & dismemberment insurance policies as well as long- and short-term disability benefit coverages.
• eligible to participate in PEMCO’s 401(k) plan, which includes a generous employer match (2 for 1 on the first 6% employee pre-tax and/or Roth deferral, up to federal maximums).
PEMCO provides the following paid leave programs for benefits-eligible employees in their first year of PEMCO employment:
• Vacation and eight (8) paid holidays.
• Granted four (4) floating holidays and up to ten (10) days of sick leave immediately upon hire (pro-rated based on hire date and full-time/part-time status).
• In addition, PEMCO provides paid time off for bereavement, jury duty, and employee volunteering in the community.
Other miscellaneous benefit programs offered by PEMCO include:
• Education Assistance Program after one year of service.
• Scholarship program for children of PEMCO employees after one year of service; children’s birthday gift program
• Flexible Spending Accounts, Employee Assistance Program, and charitable gift matching
Other compensation depending on role, contributions, and performance may include:
• Discretionary bonuses.
• Tiered sales commissions and/or incentives
Equal Employment Opportunity:
At PEMCO, we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our products, and our community to flourish. PEMCO is honored to be an equal opportunity workplace. We are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national orientation, age, citizenship, marital status, disability, gender identity, sexual orientation, or veteran status.
Accommodations
At PEMCO, people are at the heart of our business. We’re committed to providing an inclusive hiring experience for all candidates. If you need reasonable accommodations to apply for a role or participate in an interview, please contact us by email with your name, the job reference number(s), your preferred contact method, and a brief description of the accommodation needed. Requests are considered on a case by case basis in accordance with applicable disability laws, including the ADAAA.
Applicants have rights under Federal Employment Laws:
• Family and Medical Leave Act (FMLA)
• Equal Employment Opportunity (EEO)
• Employee Polygraph Protection Act (EPPA)
The Principal AI Platform Engineer role will build and run PEMCO's AI platform: the operational, economic, and governance layer under every AI agent and model the company uses. You own the stack end to end, from GPU compute through model serving, retrieval, and orchestration up to observability and governance, and you are expected to be capable of building and operating it on-premise when the economics justify it, not only consuming managed cloud services. This is a hands-on principal role with organizational influence: you build the systems yourself while setting the standards others build against.