Senior Applied Artificial Intelligence Engineer, Snowflake

• Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion
• Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows
• Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems, and using evals to systematically improve quality over time
• Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to both technical and executive stakeholders
• Comfort with ambiguity and the ability to independently structure and execute on complex, open-ended problems
• 5+ years of professional software engineering experience
• Experience in a customer-facing technical role
• Willingness to travel

Desirable

• At Snowflake, we are building a high-impact team to help the world's most innovative companies unlock the power of AI
• As a Senior Applied AI Engineer on our Cortex AI team, you will be a hands-on technical leader and trusted partner to our most strategic customers
• You will own the end-to-end delivery of enterprise AI programs, leading a team of 2–4 engineers while staying deeply technical yourself
• You will set the technical direction for your customer engagements, mentor your team, and serve as the senior technical voice at the intersection of product, engineering, and customer success
• Lead Customer Programs: Own the full lifecycle of complex, multi-engineer AI engagements – from scoping and architecture through deployment, monitoring, and handoff. Be accountable for delivery quality and customer outcomes for the projects you lead
• Own AI Quality: Define what "good" means for each engagement. Translate ambiguous customer goals into measurable quality metrics, evaluation frameworks, and golden datasets – then run systematic eval loops to hill-climb on agent quality, catch regressions before customers do, and continuously raise the bar on accuracy, faithfulness, and safety. Set the standard for how the team measures and improves AI systems in production
• Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship to a team of 2–6 Applied AI Engineers. Review designs and code, unblock teammates, and actively develop their skills and careers
• Deliver with Velocity: Remain a hands-on contributor – designing, iterating, and shipping high-quality ML pipelines and agentic AI solutions alongside your team. Translate ambiguous business objectives into robust, scalable, and performant solutions
• Productionize AI at Scale: Own the full implementation lifecycle for AI solutions, from prototype through deployment, monitoring, and optimization in secure, large-scale production environments. Build the safety guardrails, observability, and human-review workflows that keep AI applications reliable and trustworthy – and close the loop from production traces and user feedback back into your evals so quality compounds over time
• Be a Strategic Technical Advisor: Serve as a senior technical advisor to customer data science and engineering leadership. Set the standard for how Snowflake AI is deployed and articulate complex technical concepts to both technical and executive stakeholders
• Collaborate to Innovate: Work cross-functionally with Snowflake's Product and Engineering teams, bringing real-world patterns and feedback from the field to directly shape the future of Snowflake's AI platform
• Drive Compounding Outcomes: Identify recurring deployment patterns and turn them into reusable assets – reference architectures, evaluation harnesses, and product feedback that scale Snowflake's impact across customers