Engineer Jobs in Utah, UT

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Today

Staff Applied AI Engineer

Ivanti Utah, UT

Utah, UT

Today

Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Build end-to-end, production AI systems that connect unified structured and unstructured data to decision-grade analytics and agentic LLM solutions, with architectural ownership across the enterprise.

Responsibilities

Unify structured and unstructured data by creating pipelines that bring together Snowflake datasets (plus adjacent warehouses and lakes) and unstructured sources including text, documents, logs, and transcripts into modeling-ready datasets.
Deliver decision-grade analytics, including customer churn analytics with quantified uncertainty (confidence or credible intervals) and clear communication on what the metrics support and where assumptions limit interpretation.
Build predictive and prescriptive models that move from forecasting to action by developing propensity and optimization or recommendation systems that drive concrete business decisions.
Engineer agentic AI systems by designing and shipping LLM workflows and agents that are token-efficient through context management, retrieval and caching strategies, model routing, and evaluation harnesses that protect cost and latency without sacrificing quality.
Architect for the enterprise by defining reference architectures, integration patterns, and governance standards across ingestion, model development, MLOps/LLMOps, security, and observability, supported with diagrams and documentation.
Own quality and reliability by establishing 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 needs into technical solutions and explaining technical tradeoffs to non-technical stakeholders.

Requirements

Hands-on data engineering experience with Snowflake (including modeling, performance, and cost management) plus SQL, along with experience wrangling unstructured data.
Applied statistics skills to build churn or retention models and express uncertainty correctly using confidence or credible intervals, including a clear understanding of underlying assumptions.
Demonstrated experience building predictive and prescriptive analytics that shipped and influenced decisions.
Production experience with LLM and agentic systems using frameworks such as LangGraph, Claude Agent SDK, CrewAI, or custom orchestrators, with a track record of optimizing token efficiency, cost, and latency.
Production RAG experience is strongly expected at senior+ level, including chunking, hybrid search, reranking, and retrieval evaluation.
Architecture strengths, including the ability to design and document end-to-end systems and patterns others can build on, with evidence supporting design choices.
Strong Python and a software engineering mindset covering testing, version control, and CI/CD.
Excellent written and verbal communication with 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 supporting work-life balance, whether working from home or in the office.
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 with access to best-in-class learning tools and programs.
Equity and belonging, with an emphasis on valuing every voice.

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 such as quantization, model routing, caching, and vLLM or TensorRT-style serving.
MLOps/LLMOps tooling experience and platform-building.
Domain experience in your industry, and prior experience owning AI strategy or 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.
Launch a churn analytics offering 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.
Publish and get adoption of a documented reference architecture and standards across teams.

About the Role

Full-stack data and AI builder role spanning raw data ingestion, defensible analytics, and production AI systems.
Architect and implement both the analytics and agentic systems, while defining the reference architectures, patterns, and standards the organization can build on.
Emphasis on architectural ownership and systems that are cost-efficient by design.

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