AI Engineer

Mutual of Enumclaw Insurance•Seattle, WA

11d•Onsite

About The Position

Mutual of Enumclaw is hiring its first dedicated AI Engineer to connect vendor AI platforms and foundation models to the company’s data and core systems, building trustworthy solutions for underwriting, claims, and agent workflows. The role follows a buy-first approach: integrating and configuring vendor tools rather than building models from scratch, and helping the company reduce reliance on consultants for AI integrations and changes. This is an onsite position in Seattle, Washington. The AI Engineer will deliver initial AI use cases, ground AI responses in company documents using retrieval-augmented generation (RAG), and work with data engineering and business experts to prepare content and evaluate results. The role also implements privacy, safety, governance, and audit controls; monitors and improves production solutions; assesses vendor capabilities; and coaches IT colleagues on AI engineering practices. In the first 12 months, success means integrating and launching one or two vendor AI use cases in production, establishing evaluation tests for each live solution, implementing governance-aligned guardrails and audit logging, creating reusable integration and RAG patterns, and tracking measurable business results.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
4–6+ years of software engineering experience, including 1–2+ years building applications with large language models or other AI services.
Strong Python skills.
Experience with APIs, system integration, and cloud services.
Hands-on experience with retrieval-augmented generation, vector search, and prompt design.
Experience testing and evaluating AI outputs for accuracy and safety.
Understanding of AI risks, including hallucinations, bias, and data leakage, and how to control them.
Clear communication with technical and business partners.

Nice To Haves

Insurance or financial services experience, especially in underwriting or claims.
Experience with Azure AI or AWS Bedrock and configuring enterprise AI platforms.
Experience with document processing, such as extracting data from PDFs, emails, and forms.
Experience working in a regulated environment with audit and compliance requirements.
Experience with agentic AI frameworks and multi-step workflows.
Familiarity with insurance core platforms such as Guidewire or Duck Creek.
Cloud or AI certifications.

Responsibilities

Connect vendor AI platforms and foundation models, including services such as Azure AI or AWS Bedrock, to policy, claims, billing, and agent systems using APIs.
Deliver initial AI use cases, including underwriting submission intake, claims intake and summarization, and assistants for agents and employees.
Configure and extend vendor AI tools to fit existing business workflows.
Build retrieval-augmented generation (RAG) so AI can answer from underwriting guidelines, policy forms, procedures, and claims documents.
Partner with data engineering to prepare, structure, and maintain the content and data used by AI solutions.
Build evaluation tests to measure accuracy, consistency, and safety before launch and after vendor model updates.
Work with underwriting and claims experts to define correct answers and review AI outputs.
Implement controls to protect personal and policyholder data, block prohibited responses, and route uncertain cases to a person.
Log AI inputs, outputs, and decisions to support audit and regulatory review, working with the AI Governance lead.
Apply MOE’s AI governance standards and help shape them through hands-on experience.
Monitor production AI solutions for quality, response time, and cost, and resolve issues promptly.
Retest and adjust solutions when vendors release new model versions.
Document solutions so others can support and extend them.
Translate business problems from underwriting, claims, and agency teams into practical AI solutions.
Evaluate vendor AI capabilities technically and advise on build, buy, or configure decisions.
Coach IT colleagues on AI engineering practices.