Applied AI Engineer

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 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. Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.