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Snowflake is seeking a Forward Deployed Engineer to join the Frontier Engineering team, working at the intersection of enterprise AI and data platforms. In this role, you will be a hands-on technical builder and strategic partner to Snowflake's most innovative customers, architecting and deploying production-grade AI systems using Snowpark, Cortex, and native LLM capabilities.
You will own the end-to-end lifecycle of AI solutions, from prototype through production deployment. Key responsibilities include: building enterprise-grade AI applications and sophisticated AI agents that solve complex business challenges; defining quality metrics and evaluation frameworks for LLM and agent systems, then systematically improving performance through structured eval loops; translating ambiguous customer objectives into robust, scalable Python and SQL solutions; productionizing AI at scale with safety guardrails, observability, and human-review workflows; partnering directly with customer data science and engineering teams as a trusted technical advisor; and collaborating cross-functionally with Snowflake's Product and Engineering teams to influence the AI platform roadmap based on real-world field feedback.
The role requires at least 25% travel time to work onsite with strategic customers. You will operate in a fast-paced, experimental environment where you rapidly test emerging AI capabilities and move with a low-ego, collaborative mindset.
REQUIREMENTS:
Minimum Qualifications:
- Bachelor's degree in Computer Science, Engineering, related technical field, or equivalent practical experience
- 3+ years of professional software engineering experience
- Willingness to travel (at least 25% of time)
- Proven experience building applications using LLMs, especially RAG and agentic workflows
- Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, using evals to systematically improve quality
- Excellent problem-solving and communication skills; ability to articulate complex technical concepts to diverse stakeholders
- Comfort with ambiguity and desire to thrive in fast-paced, ever-changing Generative AI environment
Preferred Qualifications:
- Experience building eval sets from production traces and synthetic data; running structured experimentation (A/B tests, ablations, offline evals) to compare prompts, models, or agent architectures
- Familiarity with eval and observability tooling (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 MLOps lifecycle, including model deployment, monitoring, and evaluation in cloud environments (AWS, Azure, GCP)
- Familiarity with core data science libraries and tools (pandas, numpy, Snowpark)
- Experience in customer-facing technical role (solutions architect, sales engineer, professional services)
- Startup experience
