AI Solutions Engineering - Software Engineer

What you’ll do

• Find high-leverage opportunities: Run listening tours and current-state assessments across functions. Identify and prioritize the workflows where AI can deliver outsized impact, weighing impact potential against feasibility, and focus on opportunities that can materially bend the revenue/opex curve.
• Prototype and validate: Take prioritized use cases from idea to working prototype — internal workflow automations, reusable prompt systems and templates, AI-enabled tools and assistants, and early workflow integrations — to test whether an opportunity is worth broader investment.
• Partner across the business: Work directly with functional owners, the AI Enablement Council, and AI Ambassadors to understand real workflow needs, validate selected opportunities, and build the business case and evidence base needed to inform investment decisions.
• Build responsibly: Build and test using approved tools and established Legal and Security review pathways. Validate outputs, ensure clear human review, and maintain strong data hygiene.
• Make it visible and reusable: Document what you built, the problem it solves, and what you learned. Share your work across the company through Builder Hours, demos, and showcases, and surface recurring technical needs and reuse opportunities back to the AI Enablement Council.
• Set the standard for internal AI building: Model responsible, high-quality AI building so that what you prototype can be handed off, scaled, and maintained by the functional experts who own the workflow.

What we’re looking for

• Minimum of 4 years building software, automation, or internal tooling, with excellent problem-solving skills and the ability to drive ambiguous, zero-to-one work independently.
• Hands-on experience using AI tools (e.g., Claude Code, Copilot, Cursor) in real work, and building on top of LLM APIs — tool/function calling, agents, prompt and context engineering, RAG, and patterns like MCP.
• Able to wire together APIs, automations, and integrations across the SaaS systems a company runs on (e.g., Slack, Google Workspace, GitHub, Linear) into reliable internal tools and prototypes.
• Comfortable talking with non-technical stakeholders, framing problems, sizing impact, and prioritizing by impact and feasibility — translating fuzzy needs into shippable prototypes.
• Programming proficiency in at least one language, with strong preference for Go and/or Python.
• Respects security, legal, and data-handling guardrails, and treats responsible AI use as part of doing the job well.
• Strong communication skills, proficiency in English, and the ability to work autonomously in a remote, globally distributed team.

Nice to haves

• Experience building agent skills, MCP servers, or orchestration and automation frameworks.
• A track record of turning internal workflow ideas into tools that teams actually adopt.
• Experience in internal consulting, solutions engineering, or forward-deployed/applied roles.
• Actively involved in the open-source community.
• Interest in software supply chain security, secure SDLC, or responsible AI governance.