Forward Deployed Engineer, Applied AI

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is about empowering enterprises to achieve their full potential – and people too. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology – and careers – to the next level. Where Data Does More. Join the Snowflake team. At Snowflake, we are building a high-impact team to help the world's most innovative companies unlock the power of AI. As an Forward Deployed Engineer, Applied AI on our Cortex AI team, you will be a hands-on builder and a key technical partner to our most strategic customers, placing you at the forefront of the enterprise AI revolution. You won't just work with cutting-edge technology – you'll deploy it to solve real-world business problems at scale, building production-grade AI systems using Snowpark, Cortex, and our native LLM capabilities. IN THIS ROLE AT SNOWFLAKE, YOU WILL: Build Customer Solutions: Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents. Own the end-to-end lifecycle of your workstreams – from prototype to production – directly solving customers' most complex business challenges. Own the Quality of What You Ship: Define what "good" means for the systems you build. Translate ambiguous customer goals into measurable quality metrics, evaluation frameworks, and golden datasets – then run systematic eval loops to hill-climb on agent quality, catch regressions before customers do, and continuously raise the bar on accuracy, faithfulness, and safety. Treat measurement as a first-class part of building, not an afterthought. Deliver with Velocity: Rapidly design, iterate, and ship high-quality code and pipelines. Translate ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL. Productionize AI at Scale: Own the full implementation lifecycle for your solutions – from prototype through deployment, monitoring, and optimization in secure, large-scale production environments. Build the safety guardrails, observability, and human-review workflows that keep AI applications reliable and trustworthy, and close the loop from production traces and user feedback back into your evals so quality compounds over time. Be a Technical Partner: Partner directly with customer data science and engineering teams as a hands-on technical resource and trusted advisor on how to best leverage AI for their business challenges. Collaborate to Innovate: Work cross-functionally with Snowflake's Product and Engineering teams, sharing real-world feedback from the field to directly influence the future of Snowflake's AI platform. Have the opportunity to travel: Spend at least 25% of your time onsite, working closely with Snowflake's most strategic customers. WE'RE LOOKING FOR CANDIDATES WHO HAVE: MINIMUM QUALIFICATIONS - Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience. - 3+ years of professional software engineering experience. - Willingness to travel. - Proven experience building applications using LLMs, especially with technologies like RAG and agentic workflows. - Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, and using evals to systematically improve quality. - Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to diverse stakeholders. - Comfort with ambiguity and a desire to thrive in a fast-paced, ever-changing Generative AI environment. PREFERRED QUALIFICATIONS - Experience building eval sets from production traces and synthetic data, and running structured experimentation (A/B tests, ablations, offline evals) to compare prompts, models, or agent architectures. - Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or experience building custom eval harnesses. - Experience with failure-mode analysis on agent or RAG systems – categorizing errors (hallucination, retrieval miss, planning failure, tool misuse) and driving each down with targeted evals. - Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evalu