Staff Applied AI Engineer

The Staff Applied AI Engineer role at Ivanti focuses on designing and delivering production AI systems that turn unified structured and unstructured data into decision-grade analytics and agentic LLM solutions. The position includes architectural ownership across the enterprise, with an emphasis on reliability, governance, and token-efficient workflows.

Responsibilities

• Unify structured and unstructured data by building pipelines that combine Snowflake datasets (and adjacent warehouses or lakes) with unstructured sources such as text, documents, logs, and transcripts into modeling-ready datasets.
• Deliver decision-grade analytics, including customer churn analytics with properly quantified uncertainty using confidence or credible intervals, along with clear communication of what the metrics can and cannot support.
• Build predictive and prescriptive models that move from forecasting and propensity to optimization and recommendation systems that support concrete business actions.
• Engineer agentic AI systems by designing and shipping LLM-powered agents and workflows that prioritize token efficiency through context management, retrieval and caching strategies, model routing, and evaluation harnesses that reduce cost and latency while maintaining quality.
• Architect for the enterprise by defining reference architectures, integration patterns, and governance standards across data ingestion, model development, MLOps/LLMOps, security, and observability, supported by diagrams and documentation.
• Own quality and reliability through evaluation, monitoring, and guardrails for drift, accuracy, bias, safety, and cost across both classical ML and GenAI systems.
• Partner across the business by translating ambiguous business problems into technical solutions and explaining technical tradeoffs to non-technical stakeholders.

Requirements

• Hands-on data engineering experience with Snowflake (modeling, performance, cost management) and SQL, plus experience wrangling unstructured data.
• Applied statistics strength, including the ability to build churn or retention models and express uncertainty correctly using confidence or credible intervals, with an understanding of underlying assumptions.
• Demonstrated experience shipping predictive and prescriptive analytics that influenced decisions.
• Production experience building LLM or agentic systems, including frameworks such as LangGraph, the Claude Agent SDK, CrewAI, or custom orchestrators, with a track record of optimizing token efficiency, cost, and latency.
• Production RAG experience, including chunking, hybrid search, reranking, and retrieval evaluation; strongly expected at the senior+ level.
• Architecture capability to design and document end-to-end systems and patterns others can build on, with evidence to support decisions.
• Strong Python skills and a software-engineering mindset, including testing, version control, and CI/CD.
• Excellent written and verbal communication and comfort working asynchronously in a distributed team.

Technologies

• Snowflake, SQL, Python
• LangGraph, Claude Agent SDK, CrewAI
• RAG, CI/CD
• AWS Solutions Architect, Google Cloud Professional ML Engineer, Azure AI Engineer
• TOGAF, vLLM, TensorRT

Benefits

• Friendly flexible working model that supports excellence whether working at home or in the office, with emphasis on work-life balance.
• Competitive compensation and total rewards, including health, wellness, and financial plans for you and your family.
• Global, diverse teams with collaboration across 23+ countries.
• Learning and development support with access to best-in-class learning tools and programs.
• Equity and belonging, valuing every voice to help inform solutions.

Preferred Qualifications (Nice to Have)

• Cloud certifications (AWS Solutions Architect, Google Cloud Professional ML Engineer, Azure AI Engineer) and/or TOGAF for enterprise architecture.
• Experience with inference optimization such as quantization, model routing, and caching, including vLLM/TensorRT-style serving.
• MLOps/LLMOps tooling and platform-building experience.
• Domain experience in your industry, and prior work owning AI strategy or making build-vs-buy decisions.

What Success Looks Like (First 6–12 Months)

• Deliver a unified data foundation combining Snowflake and unstructured sources for downstream modeling.
• Build a churn analytics product trusted by the business, including quantified uncertainty and clear recommended actions.
• Ship at least one production agentic solution that reduces token spend and latency versus a naive baseline while meeting quality requirements.
• Create a documented reference architecture and standards adopted by other teams.

About the Role

This is a builder-first role for a full-stack data and AI practitioner who can move from raw data to production AI systems and the enterprise architecture that supports them. You will ingest and reason over highly structured warehouse data in Snowflake alongside unstructured inputs, then translate that work into defensible analytics and agentic AI solutions designed to be intelligent and cost-efficient. The role includes writing models and agents directly, as well as defining reference architectures, patterns, and standards so other teams can build on top of the delivered platform.

Location: Utah, UT (hybrid).