Staff Applied AI Engineer

Responsibilities

• Build pipelines combining Snowflake and unstructured data sources into modeling-ready datasets.
• Produce churn and retention analytics with confidence or credible intervals and communicate their limitations.
• Develop predictive and prescriptive models for forecasting, propensity, optimization, and recommendations.
• Design and deploy token-efficient LLM-powered agents and workflows with retrieval, caching, model routing, and evaluation systems.
• Define enterprise reference architectures, integration patterns, governance standards, diagrams, and documentation.
• Establish monitoring, evaluation, and guardrails for drift, accuracy, bias, safety, and cost across ML and GenAI systems.
• Translate ambiguous business problems into technical solutions and explain tradeoffs to non-technical stakeholders.

Requirements

• Hands-on Snowflake data engineering, SQL, data modeling, performance optimization, cost management, and unstructured-data wrangling.
• Applied statistics experience building churn or retention models and expressing uncertainty with confidence or credible intervals.
• Demonstrated experience delivering predictive and prescriptive analytics that influence business decisions.
• Production experience with LLM or agentic systems using LangGraph, the Claude Agent SDK, CrewAI, or custom orchestrators.
• Experience optimizing AI systems for token efficiency, cost, and latency.
• Strong production RAG experience, including chunking, hybrid search, reranking, and retrieval evaluation.
• Ability to design and document end-to-end architectures and reusable patterns.
• Strong Python and software-engineering practices, including testing, version control, and CI/CD.
• Excellent written and verbal communication and comfort working asynchronously in a distributed team.
• Preferred qualifications include AWS Solutions Architect, Google Cloud Professional ML Engineer, Azure AI Engineer, and/or TOGAF certifications.
• Preferred experience includes inference optimization with quantization, model routing, caching, vLLM, or TensorRT-style serving; MLOps/LLMOps tooling; platform building; and AI strategy or build-vs-buy ownership.

Benefits

• Flexible work-from-home or office working model supporting work-life balance.
• Health, wellness, and financial plans for employees and families.
• Global collaboration with teams across more than 23 countries.
• Learning and development tools and programs.
• Equity and an inclusive, equal-opportunity workplace.