AI Engineer (New Ventures)
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Reach the decision-maker — $5About the role
About monday.com monday.com is the AI work platform powering the most ambitious teams. 250,000+ customers across departments use us to bring people, workflows, and AI agents together on one flexible platform where AI doesn't just assist, it executes. We move fast, build things that matter, and foster an ownership-driven culture where you're empowered to shape how organizations work and outpace their competition. About the team New Ventures at monday.com is expanding into new, AI-native product areas, and we’re looking for an exceptional AI Engineer to join our fast-moving, high-impact team. Our team develops new products from the ground up, combining startup velocity with the scale, reach, and engineering excellence of monday.com . The team built workcanvas.com and workassets.ai , and continues to explore and launch new product ventures. As part of a small, highly talented engineering group, you’ll play a key role in shaping the AI foundations, agent architectures, and intelligent systems powering these products and future initiatives. This is a rare opportunity to define how AI-native products are built at monday.com —from core infrastructure and agent orchestration to model quality, evaluation, and continuous improvement in production . Join a tight-knit team building the next generation of collaborative, AI-powered products. We experiment quickly, validate ideas fast, and ship frequently. You’ll collaborate with senior engineers, designers, and product managers to push the boundaries of what’s possible with modern AI. About the role Architect and build AI-native systems from 0→1, including autonomous agents and intelligent workflows Design and implement agent orchestration frameworks (memory, planning, tool usage, self-reflection, and recovery) Own the full lifecycle of model quality —from experimentation and evaluation to production monitoring and continuous improvement Design and build evaluation frameworks (offline benchmarks, online experiments, A/B testing) to guide model and product decisions Monitor production AI systems , tracking quality, latency, cost, and failure modes (e.g., hallucinations, drift), and drive iterative improvements Rapidly experiment with models, prompts, and architectures, using structured evaluation to identify the best solutions Translate cutting-edge LLM capabilities into reliable, scalable, production-grade systems Collaborate closely with product, design, and full-stack engineers to shape AI-driven user experiences Take ownership of AI features end-to-end—from research and prototyping to deployment and monitoring Help define best practices for building, evaluating, and operating AI-native products across the organization Our Tech Stack React (Next.js), Node.js, TypeScript, PostgreSQL, Google Cloud Platform (GCP), Cloudflare, and modern AI/LLM tooling. Requirements Proven experience building and deploying production-grade AI systems (beyond prototypes or demos) Strong software engineering foundation with 4+ years of experience in backend or full-stack development Deep familiarity with modern AI tooling and ecosystems (LLM APIs, embeddings, vector databases, RAG pipelines) Hands-on experience designing evaluation and monitoring systems for LLM-based applications in production Experience designing or working with agent-based systems (e.g., ReAct, tool use, multi-step reasoning loops) Strong understanding of system design, scalability, and reliability in distributed environments Experience running structured experiments (e.g., A/B tests, prompt/model comparisons) and using data to drive decisions Ability to navigate ambiguity and rapidly evolving technologies Strong communication skills and a collaborative, product-oriented mindset Advantages: Experience building AI-native or agent-based products from scratch Familiarity with LLM observability tools, tracing, and debugging workflows Experience with real-time systems, WebSockets, or collaborative environments Background in rapid prototyping, experimentation, or startup-like environments #LI-DNI
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