Senior Software Engineer - AI Enablement

Workiva Inc.•Bozeman, MT

6d•$111,000 - $180,000•Hybrid

About The Position

As a Senior Software Engineer, you will be the technical engine of the AI Enablement team. Your mission is to bridge the gap between "AI-adjacent" and "AI-native" by building the tools and integrations that make AI indispensable to the PDLC. While you will contribute across our entire stack, you will be expected to own a specific domain—such as our Model Context Protocol (MCP) ecosystem, wk-ai-prototyping repo or our future automated testing agents—becoming the go-to expert for that area while maintaining the flexibility to support the broader team.

Requirements

Experience: 4+ years of related software development experience.
Technical Stack: Strong proficiency in back-end services, proxy servers, and API integrations. Experience with Python, Go, or TypeScript is preferred.
AI Literacy: Extensive experience using AI-augmented IDEs (e.g., Cursor, Claude Code) to maintain high throughput and quality.
Communication: Ability to translate complex technical implementations into clear, actionable documentation for the broader engineering org.

Responsibilities

Architecture & Technical Mastery (Primary Focus): Domain Ownership: Take deep ownership of a specific technical domain (e.g., our MCP server infrastructure or wk-ai-prototyping repo). Be the primary maintainer and visionary for this component.
Build & Deploy: Architect and maintain internal MCP servers (integrating Jira, Splunk, Datadog, etc.) and deploy cloud-hosted autonomous agents within FedRAMP boundaries.
Resilient Implementation: Independently deliver software solutions through a set of milestones, ensuring code is sustainable, reusable, and optimized for an AI-augmented environment.
Tooling Evaluation: Conduct deep technical spikes on emerging AI tools (e.g., Qodo, Cursor, Claude Code) to ensure compatibility with Workiva’s architecture and security standards.
Process Integration & Consulting (Growth Path): Best Practice Documentation: Actively surface and document "Golden Paths"—creating playbooks that standardize how engineers utilize AI to reduce KTLO (Bugfixes, CVEs, Migrations).
Light Consulting: Serve as a tactical consultant for product teams, helping them integrate AI agents into their specific workflows and troubleshooting technical blockers.
Data-Driven Quality: Implement and monitor metrics (using tools like Mstone) to measure the impact of AI on team velocity and code quality.
Evangelism & Mentorship: Skill Mastery: Level up team members by providing guidance on AI-augmented development techniques and conducting "Review-First" code reviews.
Cultural Catalyst: Showcase successful use cases and strategic wins in engineering syncs to encourage organic AI adoption.