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This role is located in Budapest, Hungary - We are a hybrid work environment and are in the office 3+ days/per week. 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.” About You You're a builder who's been inside the LLM/Agentic AI world, and you've turned that experience into tools that engineers rely on. You think agentic AI and developers working together is one of the most interesting opportunities of this decade, and you want your fingerprints on the tools, standards, and platform that make that possible here. You understand the full process of shipping an agent into production: prompting, tool and MCP design, reliability, evals, the UX of human-AI handoffs, and the plumbing in between. Key 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 What Skills or Experience Do I Need? 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 Key Collaborators Product Engineering Teams AKA Our Customers Infrastructure & DevOps Teams QA Working At Tulip We know even great candidates experience imposter syndrome. Even if you don’t match every requirement, applying gives you the opportunity to be considered. We’re building a strong, diverse team that values hard work, families, and personal well-being. Benefits of working with us include: KFT Employees Direct impact on product and culture Private Healthcare insurance Company equity Virtual company events and happy hours Flexible work schedule and unlimited vacation policy Team Lunches in KFT Hub and office snacks Learning & Development benefits HU Eyeglass Benefit An inclusive, dog-friendly office with diverse and inspiring colleagues It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Please note that we may use AI-based tools to support parts of our hiring process. All data processing is carried out in compliance with local data protection laws, ensuring all personal candidate information is handled securely and ethically.
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In your interview, expect this: 'Walk me through the last time you had to debug something that took you more than an hour. What was it, what did you try first, and what would you do differently?' Hiring managers ask this to see if you think systematically or just try random fixes. Have a real example ready with specifics.