Senior Applied AI Engineer

Responsibilities

• Lead the full lifecycle of complex, multi-engineer AI engagements—from scoping and architecture through deployment, monitoring, and handoff—for enterprise customers
• Define quality metrics, evaluation frameworks, and golden datasets for LLM/agent systems; run systematic eval loops to improve accuracy, faithfulness, and safety
• Provide day-to-day technical leadership and mentorship to a team of 2–6 Applied AI Engineers; review designs and code
• Design, iterate, and ship high-quality ML pipelines and agentic AI solutions while staying hands-on
• Own the implementation lifecycle for AI solutions from prototype through production deployment, monitoring, and optimization in secure, large-scale environments
• Build safety guardrails, observability, and human-review workflows; close the loop from production traces and user feedback into evaluations
• Serve as a senior technical advisor to customer data science and engineering leadership; articulate complex technical concepts to technical and executive stakeholders
• Collaborate cross-functionally with Product and Engineering teams to shape Snowflake’s AI platform using real-world customer patterns
• Identify recurring deployment patterns and create reusable assets such as reference architectures and evaluation harnesses
• Travel at least 25% of the time to work onsite with strategic customers

Requirements

• Demonstrated experience leading technical projects or teams, including setting technical direction and driving delivery
• Proven experience building and productionizing applications using LLMs, especially with RAG and agentic workflows
• Hands-on experience defining quality metrics and evaluation frameworks for LLM/agent systems and using evals to improve quality over time
• Excellent problem-solving and communication skills with the ability to articulate complex concepts to technical and executive stakeholders
• Comfort with ambiguity and ability to 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

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 (Braintrust, LangSmith, Arize, Weave, Promptfoo) or building custom eval harnesses
• Experience with failure-mode analysis on agent or RAG systems (hallucination, retrieval miss, planning failure, tool misuse)
• 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 (pandas, numpy, Snowpark)
• Startup or high-growth environment experience