Staff ML Engineer

DataOps / ML & AI: What's the mission? Factorial is building a world-class data and AI platform to help teams make smarter decisions, automate complex workflows, and deliver more value to our customers. As a Staff ML Engineer, you'll set the technical direction for how we design, build, and ship AI-powered systems across Factorial. You'll own our most ambitious AI initiatives end-to-end—LLM applications, agents, and assistants—and make them accurate, reliable, explainable, and production-ready at scale. You'll be a force multiplier: raising the engineering bar, mentoring other engineers, and partnering with Product, Engineering, Data, Operations, Sales, and Customer Experience to turn the biggest business opportunities into robust AI solutions—balancing speed, impact, correctness, and long-term maintainability.

The role 📜 You'll be part of the Data Ops – ML & AI team, working within a fast-growing Engineering and Data organization that builds high-impact systems across Factorial. You will identify, design, build, and deploy AI solutions across the full lifecycle: from problem framing and prototyping to development, productionization, evaluation, monitoring, and continuous improvement—and you'll define the patterns and standards the rest of

the team builds on. A key part of

the role is supporting our Data Scientists and Analytics team across the ML lifecycle—helping them take models and experiments from notebook to production, providing the tooling, pipelines, and best practices they need, and acting as the technical partner who turns their work into reliable, maintainable systems. You'll spend most of your time building with LLMs and modern AI frameworks—designing RAG pipelines, tool-using agents, and assistants with LangChain and the surrounding ecosystem. You'll be expected to understand when an LLM-based approach is the right tool—and when a simpler, more deterministic solution is better. You'll partner with stakeholders across the company to understand their needs, define success metrics, communicate trade-offs clearly, and ensure the solutions we build are useful, adopted, and measurable. You'll take ownership of projects from inception to production, working autonomously while leading cross-functional efforts alongside Data Engineers, Software Engineers, Product Managers, and business teams—and you'll help others do the same through technical mentorship and design reviews. Our stack & the day-to-day 🧑‍💻 We predominantly use Python, SQL, and modern AI tooling to build, evaluate, and deploy AI solutions. You'll write production-grade Python and work extensively with LangChain and the LLM ecosystem—building RAG systems, agents, orchestration, prompting strategies, and evaluation harnesses. You'll turn business questions into AI capabilities: defining the right interfaces, retrieval strategies, prompts, tools, and guardrails, and validating them against clear, measurable outcomes. You'll develop LLM-powered applications and AI products, ensuring they are well-tested, observable, cost-aware, safe, and aligned with business needs. You'll collaborate with Engineering and Data teams to bring AI systems into production, defining clear interfaces, reliable pipelines, monitoring, and feedback loops—and establishing the architecture and best practices others reuse. On the ML lifecycle side, you'll act as the engineering backbone for our Data Scientists and Analytics team—pairing with them to productionize their models, building the shared pipelines, frameworks, and monitoring they rely on, and reviewing their work to ensure it's reliable, observable, and maintainable in production. You'll implement evaluation and monitoring practices to detect quality regressions, hallucinations, latency or cost issues, and changes in user or business behavior. You'll write documentation, share findings, join and lead technical conversations, and communicate insights clearly to both technical and non-technical stakeholders. You'll iterate in short development cycles, testing assumptions quickly, learning from results, and improving solutions based on real-world impact. Your first steps at Factorial Read a lot: Get to know Factorial's product, customers, data ecosystem, and current ML & AI initiatives. Learn a lot: Pair with team members from Data, Product, Engineering, and business teams to understand the main opportunities and challenges where AI can create value. Analyze a lot: Explore use cases, validate assumptions, prototype quickly, and help define clear success metrics and technical direction for our most impactful AI projects. Build a lot: Lead and contribute to high-impact AI workflows—LLM applications, agents, retrieval, evaluation, and stakeholder-facing solutions. Fail a lot: We encourage experimentation. We understand that trying out new ideas, failing, learning, and improving is part of