Snowflake Forward Deployed Engineer - Gps

This role focuses on building and deploying GenAI-enabled solutions, agentic platforms, and workflows within enterprise AI platforms, specifically using Snowflake technologies like Cortex AI. The engineer will prototype and deliver working AI solutions, develop scalable AI engineering patterns, and apply architecture decisions for quality, safety, latency, and cost. They will also contribute to production-quality code with strong practices in testing, CI/CD, logging, and documentation.

What you'd actually do

  1. Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
  2. Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  3. Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  4. Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  5. Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Skills

Required

  • 4+ years of experience in software engineering, data engineering, data science, or analytics engineering
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Snowflake including hands on experience with one of the following key platform technologies; Cortex AI, Cortex LLM Functions, Cortex Agents, Arctic Embed
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code
  • Ability to travel 50%, on average
  • Must be legally authorized to work in the United States without the need for employer sponsorship
  • Ability to obtain and maintain a US government security clearance

Nice to have

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
  • Experience operating within hybrid onshore/offshore teams
  • Familiarity with security, privacy, and compliance considerations

What the JD emphasized

  • GenAI-enabled solutions
  • agentic platforms
  • workflows across enterprise AI platforms
  • production-quality code

Other signals

  • GenAI-enabled solutions
  • agentic platforms
  • workflows across enterprise AI platforms
  • production-quality code