Staff Applied AI Engineer
A3c41b8b71eff8c4 · South Jordan, Utah
About this role
**About Ivanti** Ivanti empowers organizations to manage and secure technology smarter through our AI-powered Ivanti Neurons platform—helping IT and Security teams reduce complexity, work proactively, and deliver better outcomes at scale. **About the Role** We’re looking for a rare, full-stack data and AI practitioner who can move fluidly from raw data to production AI systems and the enterprise architecture that supports them. You’ll ingest and reason over structured and unstructured sources, turn them into defensible analytics, and ship agentic AI solutions that are both intelligent and ruthlessly cost-efficient. This is a builder-first role with real architectural ownership: you’ll write the models and agents yourself, and define the reference architectures, patterns, and standards the rest of the organization can build on. --- ## What You’ll Do - **Unify structured and unstructured data**: Build pipelines that pull structured data from Snowflake (and adjacent warehouses/lakes) alongside unstructured sources (text, documents, logs, transcripts) into modeling-ready datasets. - **Deliver decision-grade analytics**: Produce customer churn analytics with properly quantified uncertainty (confidence/credible intervals), not just point estimates. - **Build predictive and prescriptive models**: Forecasting, propensity, and optimization/recommendation systems that drive concrete business actions. - **Engineer agentic AI systems**: Design and ship LLM-powered agents and workflows with token-efficient design (context management, retrieval/caching, model routing, and evaluation harnesses). - **Architect for the enterprise**: Define reference architectures, integration patterns, and governance standards across ingestion, model development, MLOps/LLMOps, security, and observability. - **Own quality and reliability**: Establish evaluation, monitoring, and guardrails for drift, accuracy, bias, safety, and cost across classical ML and GenAI. - **Partner across the business**: Translate ambiguous business problems into technical solutions and communicate tradeoffs to non-technical stakeholders. --- ## What You Bring (Required) - Strong hands-on data engineering with **Snowflake** (modeling, performance, cost management) and **SQL**, plus experience wrangling unstructured data. - Solid applied statistics: build churn/retention models and correctly express **uncertainty with confidence/credible intervals**, including understanding assumptions. - Demonstrated experience building **predictive and prescriptive analytics** that shipped and influenced decisions. - Production experience with **LLM/agentic systems** (e.g., LangGraph, Claude Agent SDK, CrewAI, or custom orchestrators), with a track record of **optimizing for token efficiency, cost, and latency**. - Production **RAG** experience (chunking, hybrid search, reranking, retrieval evals) strongly expected at senior+ level. - **Architecture chops**: design and document end-to-end systems/patterns others can build on, and defend decisions with evidence. - Strong **Python** and a software-engineering mindset (testing, version control, CI/CD). - Excellent written and verbal communication; comfortable working asynchronously in a distributed team. --- ## Nice to Have (Preferred) - Cloud certifications (AWS Solutions Architect, Google Cloud Professional ML Engineer, Azure AI Engineer) and/or **TOGAF** for enterprise architecture. - Experience with inference optimization (quantization, model routing,
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