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SnowflakeSnowflakeMenlo Park, CA

Applied AI Engineer

Hands-on Applied AI Engineer on the Cortex AI team building and deploying production-grade AI agents and solutions for enterprise customers using Snowpark, Cortex, and native LLM capabilities.

126k – 182k/yr
On-site3+ YOEML Engineering

About the role

Responsibilities

  • Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents
  • Own the end-to-end lifecycle of workstreams from prototype to production, solving customers' most complex business challenges
  • Define quality metrics, evaluation frameworks, and golden datasets for AI systems
  • Run systematic eval loops to improve agent quality, catch regressions, and raise the bar on accuracy, faithfulness, and safety
  • Rapidly design, iterate, and ship high-quality code and pipelines using Python and SQL
  • Own the full implementation lifecycle including deployment, monitoring, and optimization in secure, large-scale production environments
  • Build safety guardrails, observability, and human-review workflows for AI applications
  • Close the loop from production traces and user feedback back into evaluations
  • Partner directly with customer data science and engineering teams as a hands-on technical resource
  • Work cross-functionally with Product and Engineering teams to share real-world feedback and influence the AI platform
  • Spend at least 25% of time onsite with strategic customers

Requirements

  • 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 (at least 25% onsite)
  • Proven experience building applications using LLMs, especially with RAG and agentic workflows
  • Hands-on experience defining quality metrics and running evaluations for LLM or agent systems
  • Excellent problem-solving and communication skills
  • Comfort with ambiguity and thriving in a fast-paced Generative AI environment

Nice-to-Haves

  • Experience building eval sets from production traces and synthetic data
  • Running structured experimentation (A/B tests, ablations, offline evals)
  • Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or building custom eval harnesses
  • Experience with failure-mode analysis on agent or RAG systems
  • Hands-on experience with the MLOps lifecycle including model deployment, monitoring, and evaluation in cloud environments (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

Skills

PythonSQLLLMsRAGAI AgentsMLOpsSnowparkAWSAzureGCP

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