Chainguard provides hardened, production-ready open-source software builds that help organizations secure their software supply chains by default.
<div class="content-intro"><p>Chainguard is the trusted source for open source. By delivering hardened, secure, and production-ready builds of all the open source software engineers and AI agents rely on, Chainguard helps organizations build faster, stay compliant, and eliminate risk. <br><br>Our customers include Fortune 500 enterprises and global industry leaders, including Anduril, Canva, Fortinet, Hewlett Packard Enterprise, OpenAI, Snap Inc., and Snowflake. <br><br>Chainguard is venture-backed by leading investors, including Amplify, IVP, Kleiner Perkins, Lightspeed Venture Partners, Mantis VC, Redpoint Ventures, Sequoia Capital, and Spark Capital.</p></div><h2><strong>The role, in a nutshell:</strong></h2> <p>Chainguard is becoming an AI company, and we’re hiring an AI Solutions Engineer to make that real in the daily work of every team. This is a dedicated, hands-on role with a clear mission: find the highest-leverage opportunities where AI can materially change the economics of how Chainguard operates — revenue, efficiency, and quality — and build the working prototypes that prove they work.</p> <p>The role is part internal consultant, part builder. You’ll partner with functional owners across the company (Engineering, GTM, Product, Operations, and beyond), the AI Enablement Council, and our AI Ambassadors to take prioritized use cases from idea to validated prototype. You’ll turn promising ideas into internal workflow automations, reusable prompt systems, AI-enabled tools and assistants, and early integrations — using approved tools, keeping humans in the loop, and holding a high security and quality bar.</p> <p>This is an applied tooling and building role, not a research role. You’ll work with tools like Claude Code, the Anthropic API, Glean, and the systems Chainguard already runs on. Your job is to reduce the frustration of great ideas stalling for lack of capacity or technical skill — and to build the evidence base that tells the company where to invest next.</p> <h2><strong>What you’ll do:</strong></h2> <ul> <li><strong>Find high-leverage opportunities: </strong>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.</li> <li><strong>Prototype and validate: </strong>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.</li> <li><strong>Partner across the business: </strong>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.</li> <li><strong>Build responsibly: </strong>Build and test using approved tools and established Legal and Security review pathways. Validate outputs, ensure clear human review, and maintain strong data hygiene. No AI tool is used on Chainguard, customer, or personal data until it has cleared review.</li> <li><strong>Make it visible and reusable: </strong>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.</li> <li><strong>Set the standard for internal AI building: </strong>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.</li> </ul> <h2><strong>What we’re looking for:</strong></h2> <ul> <li><strong>Experience: </strong>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.</li> <li><strong>Applied AI tooling: </strong>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.</li> <li><strong>Builder and integrator: </strong>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.</li> <li><strong>Product and consulting sense: </strong>Comfortable talking with non-technical stakeholders, framing problems, sizing impact, and prioritizing by impact and feasibility — translating fuzzy needs into shippable prototypes.</li> <li><strong>Development: </strong>Programming proficiency in at least one language, with strong preference for Go and/or Python.</li> <li><strong>Responsible-use mindset: </strong>Respects security, legal, and data-handling guardrails, and treats responsible AI use as part of doing the job well.</li> <li><strong>Communication: </strong>Strong communication skills, proficiency in English, and the ability to work autonomously in a remote, globally distributed team — taking initiative and seeking help proactively when needed.</li> </ul> <p><em>This role is about applying AI tooling and building internal prototypes across the business — not training, fine-tuning, or building foundation models from scratch. It also does not own product roadmap work, independent tool purchasing, or any path that bypasses Legal and Security review.</em></p> <h2><strong>Nice to haves:</strong></h2> <ul> <li><strong>Agent ecosystems: </strong>Experience building agent skills, MCP servers, or orchestration and automation frameworks.</li> <li><strong>From idea to adoption: </strong>A track record of turning internal workflow ideas into tools that teams actually adopt.</li