Forward Deployed Engineer, Applied AI

IN THIS ROLE AT SNOWFLAKE, YOU WILL:

Build Customer Solutions
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Architect, build, and deploy enterprise‑grade AI solutions, including sophisticated AI agents. Own the end‑to‑end lifecycle of your work streams – from prototype to production – directly solving customers' most complex business challenges.

Own the Quality of What You Ship
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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
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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
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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
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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
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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
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Spend at least 25% of your time onsite, working closely with Snowflake's most strategic customers.

• 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.

• 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, Lang Smith, 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 evaluation in a cloud environment (AWS, Azure, or GCP).

• Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark).

• Experience in a customer‑facing technical role (e.g., solutions architect, sales engineer, or professional services).

• Startup experience.