AI Enablement Engineer- Developer Experience

Tulip Interfaces•Somerville, MA

1d•Hybrid

About The Position

Tulip, the leader in AI-native frontline operations, is helping companies around the world equip their workforce with composable, connected apps, leading to higher quality work, improved efficiency, and end-to-end traceability across operations. Tulip’s cloud-native, no-code platform, powered by embedded AI, is driving the digital transformation of industrial environments through composable, human-centric solutions that go beyond disrupting the Manufacturing Execution System (MES) category. A spinoff out of MIT, Tulip is headquartered in Somerville, MA, with offices in Germany, Hungary, Singapore, Israel and Japan. Tulip has been recognized as a World Economic Forum Global Innovator, a 2024 Deloitte Technology Fast award winner, one of Energage’s Top Workplaces USA, and one of Built In Boston’s “Best Places to Work” and “Best Midsize Places to Work.”

Requirements

5+ years of software engineering experience, ideally with a focus on developer tooling, platform engineering, or internal-facing/DevX systems
Meaningful hands-on experience building with AI/LLMs and shipping agentic solutions. Whether that's a career focus, or a more recent but deep pivot into the space
Proven ability to build internal tools that make powerful AI capabilities accessible to engineers, including the judgment to know when to build vs. buy, and when to standardize vs. let teams experiment
A natural collaborator who earns trust with engineers quickly, understands real engineering pain points, and co-creates solutions that get adopted rather than shelved
Strong full-stack proficiency (TypeScript and related ecosystem), deep familiarity with API design and integration (RESTful services, MCP, and similar), and comfort working across a modern engineering tech stack
Deep hands-on experience with LLMs, prompt engineering, agent frameworks, RAG, and tool-use/function-calling patterns. Production experience with AWS, modern CI/CD practices, and a genuine understanding of AI safety and security, especially around code execution and access to internal systems
Experience with or strong interest in developer experience fundamentals: reducing friction, measuring engineering productivity, and treating internal tooling like a product with real users
Bachelor's degree in Computer Science, Engineering, or equivalent work experience

Responsibilities

Partner with engineering teams to identify the highest-leverage AI opportunities in the developer workflow: from code generation and review to testing, debugging, and internal tooling.
Build and maintain the internal agentic AI platform engineers rely on daily: skills, plugins, MCP servers, and integrations across our tech stack (source control, CI/CD, observability, ticketing, and more)
Own foundational architecture and standards for how agents and model releases are evaluated, and deployed internally: including reliability, evals, efficiency, and secure tool access patterns
Build and improve the onboarding, documentation, and discovery layer that helps engineers find and adopt the right AI tools (skills catalogs, guides, internal wikis) rather than reinventing them
Stay plugged into agentic AI and developer-tooling trends externally to shape internal standards and roadmap decisions
Meaningfully contribute to organizational AI enablement for engineering, including internal learning programs, office hours, and change management as new tools roll out
Instrument and measure adoption and impact of internal AI tooling, and use that data to prioritize what to build next