Applied AI Engineer

Join Snowflake, a leading cloud data platform, as an Applied AI Engineer. In this role, you will be a hands-on builder and key technical partner to our most strategic customers, working at the forefront of the enterprise AI revolution. You will architect, build, and deploy enterprise-grade AI solutions, own the quality of your work, and deliver with velocity. You will also partner directly with customer data science and engineering teams, collaborate with Snowflake's Product and Engineering teams, and have the opportunity to travel.

• Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents, and own the end-to-end lifecycle of your workstreams.
• Define what "good" means for the systems you build, translate ambiguous customer goals into measurable quality metrics, and run systematic evaluation loops to improve quality.
• Rapidly design, iterate, and ship high-quality code and pipelines, translating ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL.
• Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, and using evals to systematically improve quality
• Willingness to travel
• 3+ years of professional software engineering experience
• Comfort with ambiguity and a desire to thrive in a fast-paced, ever-changing Generative AI environment
• Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to diverse stakeholders
• Proven experience building applications using LLMs, especially with technologies like RAG and agentic workflows
• Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience
• 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 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