Principal Engineer, Applied AI

1-800 Contacts•Draper, UT

2d•Hybrid

About The Position

SeekWell is the parent company of 1-800 Contacts, Luna, and The Framery. Our goal is to make it simpler, easier, and more accessible for people to get the vision care they need. We maintain the legendary, award-winning culture 1-800 Contacts started almost 30 years ago and continue to develop innovative, pioneering products and businesses that make consumers cheer and optometrists squirm. We’re owned by KKR – one of the world’s largest and most successful private equity investment companies. We’ve built an excellent company and changed an industry by putting the customer first, always. The best is yet to come! Why you want this job AI is changing how software gets built—and we’re leaning in. This role is a chance to shape how SeekWell engineers use AI day to day, so teams can move faster, ship with confidence, and keep quality high as we scale. You’ll own the enablement strategy, the standards, and the guardrails that turn experimentation into real leverage across the SDLC. If you enjoy translating emerging tech into practical patterns, mentoring senior engineers, and measuring outcomes—not hype—you’ll have a huge platform to make a lasting impact here.

Requirements

Strategic technical leadership: Proven track record driving large-scale technical and/or cultural change across multiple engineering teams without direct hierarchical authority.
Hands-on GenAI expertise: Deep experience with LLMs, RAG, and the evolving ecosystem of AI developer tools (e.g., Cursor, GitHub Copilot, and specialized agents).
Protocols and integration: Experience designing or adopting standards that connect AI systems to internal data, tools, and production environments.
Developer experience and automation: Background in DevOps and/or platform engineering with a focus on removing friction from the developer experience.

Responsibilities

Scale GenAI Literacy: Design and scale enablement frameworks—playbooks, patterns, and hands-on learning paths—that allow teams to continuously upskill in AI-assisted development.
Drive AI Integration Standards: Lead the strategy for implementing protocols (such as Model Context Protocol) that standardize how AI systems interact with internal data and engineering tools.
Build AI Guardrails: Establish the technical standards and governance necessary to ensure AI implementations are secure, performant, and maintainable.
Modernize the SDLC: Systematically identify and integrate AI-driven optimizations across the software development life cycle—from automated testing to deployment.
Curate the Stack: Evaluate and curate the enterprise AI toolset, ensuring we are investing in technologies that provide real long-term leverage.
Mentor Talent: Guide Staff and Principal engineers on AI best practices, helping them navigate the transition to AI-native development.
Advise Leadership: Partner with Engineering and Product Directors to identify and prioritize the highest-impact AI use cases for their specific domains.
Measure impact: Define and evolve the metrics for AI adoption—balancing learning velocity, delivery outcomes, system quality, developer satisfaction, and long-term maintainability.
Strategic consulting: Support teams tackling complex AI integration challenges or architectural hurdles.
Tooling leadership: Lead cross-functional task forces to evaluate and pilot emerging AI protocols and vendor capabilities.
Unblock team velocity: Partner with engineering teams to troubleshoot cultural or technical friction that slows AI-tool adoption.
Architect integration patterns: Provide guidance on how teams can build custom MCP servers or AI agents to interface with domain data and legacy systems.
Share internal wins: Curate and share success blueprints from early-adopting teams to inspire and accelerate AI adoption across the organization.
Audit AI efficiency: Conduct deep-dive reviews of team workflows to identify opportunities for AI-driven automation and skill gaps to address.