Senior AI Engineer

Snowflake Snowflake · Data AI · CA-Menlo Park, United States · Data Analytics and AI

Senior AI Engineer to build AI-powered applications and workflows for Snowflake's go-to-market teams, focusing on turning ambiguous business problems into production-ready AI solutions that improve productivity and decision-making. The role involves applying LLM capabilities, working across the AI stack, and ensuring reliability, observability, and maintainability of delivered systems.

What you'd actually do

  1. Design, build, and maintain AI applications, and workflow automation tools for GTM use cases across the full product lifecycle, from problem definition and prototyping through production deployment, iteration, and ongoing support
  2. Independently scope and deliver medium-sized projects, and contribute meaningfully to larger cross-functional initiatives
  3. Translate ambiguous business needs into practical technical plans, balancing speed, maintainability, quality, and long-term architecture
  4. Partner closely with teams such as Sales, Marketing, Customer Support, Product, and Engineering to understand workflow pain points and deliver solutions that improve productivity, decision-making, and execution
  5. Apply modern LLM capabilities and Snowflake AI products in production-oriented ways to solve real business problems, including knowledge-rich workflows, internal copilots, recommendation systems, and decision-support applications

Skills

Required

  • Python
  • SQL
  • prompt design
  • evaluation
  • observability
  • orchestration
  • production hardening for AI systems
  • semantic layers
  • retrieval systems
  • knowledge-rich workflows
  • communication skills
  • bias toward shipping, learning, and iterating
  • quality mindset around testing, measurement, reliability, maintainability, and responsible use of enterprise data
  • operating effectively in fast-moving, ambiguous environments with changing priorities

Nice to have

  • Snowflake platform capabilities, including Cortex and related AI workflows
  • building internal AI assistants, recommendation features, workflow automation, or decision-support applications
  • GTM, sales productivity, marketing operations, customer support, or business process automation domains
  • trusted semantic layers, retrieval systems, and AI systems grounded in enterprise data
  • analytics engineering, data modeling, or governed enterprise data architectures
  • translating internal implementation learnings into repeatable patterns, platform requirements, or product feedback

What the JD emphasized

  • production-ready AI applications
  • production deployment
  • production hardening
  • production quality
  • production-oriented ways
  • production-grade AI applications

Other signals

  • building AI-powered applications and workflows
  • production-ready AI applications
  • applied AI instincts with practical software, data, and product judgment
  • partner closely with business stakeholders to ship systems
  • apply modern LLM capabilities and Snowflake AI products to real internal use cases
  • building high-impact GTM applications and workflows
  • improving how teams use data and AI in practice
  • shaping scalable patterns for future solutions
  • turning ambiguous business problems into production-ready AI applications
  • improve how Snowflake teams work every day
  • AI-native thinkers
  • reinvent how they work
  • treating AI as a high-trust collaborator
  • core to how you solve problems and accelerate your impact
  • rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results
  • redefine the future of how work gets done
  • build AI systems grounded in trusted enterprise data and strong engineering practices
  • attention to reliability, observability, testing, measurement, and maintainability
  • Own production quality for delivered solutions
  • debugging, monitoring, issue triage, and continuous improvement
  • turning internal usage insights, workflow learnings, and implementation challenges into actionable feedback
  • Contribute to team quality through strong documentation, reusable patterns, automation, and thoughtful engineering standards
  • Act as a go-to engineer in at least one area
  • help diagnose issues beyond your immediate domain
  • mentor more junior teammates
  • Communicate clearly with stakeholders on scope, tradeoffs, timelines, risks, and outcomes
  • Hands-on experience building and shipping applied AI or LLM-based systems for real user workflows
  • Demonstrated ability to independently own and deliver production-grade AI applications or workflow automation solutions
  • Experience with prompt design, evaluation, observability, orchestration, and production hardening for AI systems
  • Experience building systems that combine AI capabilities with semantic layers, retrieval systems, or other knowledge-rich workflows
  • Good judgment in turning ambiguous business problems into scoped, iterative deliverables
  • Experience collaborating directly with business stakeholders and translating workflow pain points into product solutions
  • Strong communication skills and a bias toward shipping, learning, and iterating
  • A quality mindset around testing, measurement, reliability, maintainability, and responsible use of enterprise data
  • Experience operating effectively in fast-moving, ambiguous environments with changing priorities