AI Solutions Engineer II: Automation & Knowledge Systems Lead

Autonomous Solutions, Inc. (ASI)

Lehi (UT)

On-site

USD 120,000 - 180,000

Full time

2 days ago

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Job summary

Autonomous Solutions, Inc. (ASI) in Utah is seeking an AI Solutions Engineer II to lead discovery-to-adoption engagements across departments, architect knowledge-capture pipelines, and mentor junior engineers.

You will define pilot scope, success metrics, and adoption plans, and serve as a senior technical authority on AI-powered workflow and knowledge systems at ASI. You will evaluate speech-to-text and LLM tooling, drive automated AI workflows from prototype to production, and mentor the next

Qualifications

Bachelor's degree in CS, software engineering, or related field.
3+ years of software development with fluency in modern AI development tools.
Proven track record delivering automation or knowledge-capture systems.
Experience building capture, transcription, and structuring pipelines using speech-to-text, LLMs, or similar tooling.
Ability to lead discovery engagements and adoption planning with engineering and operations stakeholders.
Experience producing engineering-ready requirements from unstructured field knowledge.
Active mentorship of junior engineers in a professional setting.
Experience building AI automation workflows and vector databases.
Experience with Obsidian or similar knowledge-management systems.

Responsibilities

Lead discovery-to-adoption engagements across multiple departments simultaneously.
Define pilot scope, success metrics, and adoption plans for automation and knowledge-capture projects.
Architect and own the full capture, transcription, structuring, and edge-case taxonomy pipeline for field knowledge systems.
Drive engineering-workflow automation and communications-triage programs from prototype through production handoff.
Produce engineering-ready requirements documents and queryable knowledge bases.
Mentor AI Solutions Engineers I and II on discovery methods, pipeline design, and stakeholder engagement.
Present pilot findings and adoption results to department leads and engineering leadership.
Evaluate and integrate speech-to-text, large language model (LLM), and data-structuring tooling into production pipelines.
Build fully automated AI autonomous systems for all operations.