Applied AI Engineer — Build Production AI & Data Systems

Ivanti; South Jordan (UT); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Friendly flexible working model

Competitive compensation

Global, diverse teams

Learning & development

Equity & belonging

Job summary

Ivanti is seeking a full‑stack data and AI practitioner to transform raw data into production AI solutions. You’ll own end-to-end pipelines from Snowflake to intelligent agents, defining architecture and standards for enterprise scale, while balancing cost, latency, and quality.

You’ll design models, build LLM-powered agents, and collaborate with stakeholders to translate business problems into technical solutions; a strong Python and data engineering background is essential.

Qualifications

• Hands-on data engineer with Snowflake and SQL.
• Ability to express uncertainty with confidence/credible intervals.
• Production experience with LLM/agentic systems and cost-efficient design.
• Strong Python and software engineering mindset (testing, VCS, CI/CD).
• Experience with RAG, retrieval, and scalable ML pipelines.

Responsibilities

• Unify structured and unstructured data from Snowflake and other sources into clean datasets.
• Deliver analytics with quantified uncertainty and clear business implications.
• Build predictive and prescriptive analytics that guide decisions.
• Engineer agentic AI systems and manage model workflows with cost efficiency in mind.
• Define reference architectures and governance for enterprise data and ML.
• Communicate tradeoffs and technical concepts to non-technical stakeholders.

Skills

Snowflake data engineering

SQL

Statistics and uncertainty

Python

LLM/agentic systems

Data wrangling unstructured data

Distributed teamwork

Tools

LangGraph

Claude Agent SDK

CrewAI

custom orchestrators

vLLM/TensorRT serving

Git

CI/CD

Job description

Ivanti is seeking a full‑stack data and AI practitioner to transform raw data into production AI solutions. You’ll own end-to-end pipelines from Snowflake to intelligent agents, defining architecture and standards for enterprise scale, while balancing cost, latency, and quality.

You’ll design models, build LLM-powered agents, and collaborate with stakeholders to translate business problems into technical solutions; a strong Python and data engineering background is essential.