2 дня назад
Staff Applied AI Engineer
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Applied AI Engineer (Snowflake/LLM/RAG): Building unified data foundations, decision-grade churn analytics, predictive and prescriptive models, and production agentic AI systems with an accent on statistical uncertainty, token efficiency, and enterprise architecture. Focus on designing reference architectures, optimizing retrieval and model-serving costs, and establishing evaluation, monitoring, safety, and reliability standards.
Location: South Jordan, Utah, United States; hybrid working model
Company
develops the AI-powered Neurons platform for managing, securing, and automating enterprise technology.
What you will do
- Build pipelines that combine structured Snowflake and warehouse data with unstructured text, documents, logs, and transcripts.
- Develop churn, retention, forecasting, propensity, optimization, and recommendation models that support business decisions.
- Design and ship production LLM-powered agents and workflows with efficient context management, retrieval, caching, model routing, and evaluation.
- Define enterprise reference architectures, integration patterns, governance standards, and documentation across data, ML, LLMOps, security, and observability.
- Establish monitoring and guardrails for drift, accuracy, bias, safety, and cost across classical ML and GenAI systems.
- Translate ambiguous business problems into technical solutions and communicate tradeoffs with non-technical stakeholders.
Requirements
- Hands-on experience with Snowflake data modeling, performance, cost management, SQL, and unstructured data.
- Applied statistics expertise, including churn or retention modeling and confidence or credible intervals.
- Production experience with predictive and prescriptive analytics that influenced decisions.
- Production experience with LLM or agentic systems and optimization for token efficiency, cost, and latency.
- Strong production RAG experience, including chunking, hybrid search, reranking, and retrieval evaluation.
- Strong Python, software engineering, architecture, testing, version control, CI/CD, and communication skills.
Nice to have
- Cloud certifications such as AWS Solutions Architect, Google Cloud Professional ML Engineer, or Azure AI Engineer, and/or TOGAF.
- Experience with inference optimization, including quantization, model routing, caching, vLLM, or TensorRT-style serving.
- MLOps or LLMOps tooling, platform building, AI strategy, and build-versus-buy decision experience.
Culture & Benefits
- Flexible hybrid working model supporting work-life balance.
- Health, wellness, and financial plans for employees and families.
- Collaboration with diverse teams across more than 23 countries.
- Learning tools and development programs.
- Inclusive workplace focused on equity, belonging, and equal opportunity.
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