Senior Applied AI Engineer
Lead customer AI programs on the Cortex AI team, owning end-to-end delivery of LLM and agentic solutions while mentoring 2-6 engineers and staying hands-on. Requires 5+ years of software engineering experience with proven productionization of LLMs, RAG, and evaluation frameworks.
About the job
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
Skills
LLMs, RAG, Agentic Workflows, Evaluation Frameworks, MLOps, AWS, Azure, GCP, Snowpark, Python
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