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AI & Automation Engineer Remote

Remote / Online - Candidates ideally in
Boise, Ada County, Idaho, 83701, USA
Listing for: Inverodigital
Full Time, Remote/Work from Home position
Listed on 2026-07-15
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 110000 - 170000 USD Yearly USD 110000.00 170000.00 YEAR
Job Description & How to Apply Below
Position: AI & Automation Engineer (Full-Time, Remote)

The brightest people. The latest technology. Balance.

Friends, we areat a once‑in‑a‑generation inflection point.

Every organization is talking about AI, but very few are actually making it real.
This role sits at the center of that moment
:

As an AI & Automation Engineer at Invero
, you’ll help organizations move from AI curiosity to real‑world impact. This isn’t a research role or a demo factory – you’ll be embedding AI directly into the workflows people rely on every day, helping businesses adopt, govern, and trust AI responsibly.

We’re hiring an AI & Automation Engineer to own post‑workshop execution and turn prioritized AI opportunities into working solutions inside real client environments. This is a hands‑on builder‑operator role where you’ll take ideas emerging from AI Envisioning Workshops and Copilot training and bring them to life – designing, building, and deploying solutions that actually get used.

This role is open to candidates based in Canada with legal authorization to work here.

Responsibilities

Primary focus:

  • Build and configure AI-enabled solutions that improve real workflows and outcomes. This may include:
    • Microsoft Copilot Studio / Power Platform solutions
    • Azure-based AI solutions (e.g., Azure OpenAI / Azure AI Foundry) when use cases require more flexibility and developer control

Secondary focus

  • Identifydata opportunities
    that unlock AI outcomes and help drive pragmatic execution when needed (e.g., data readiness, structure, access, lightweight transformations).
Day to Day

1) Build and configure AI-enabled solutions (Primary)

  • Build, configure, and iterate AI-enabled solutions using the most appropriate approach for the use case:
    • Copilot Studio / Power Platform for fast, governed workflow automation and M365-grounded experiences
    • Azure AI (Azure OpenAI / Azure AI Foundry and related services) for deeper integrations, custom orchestration, advanced retrieval (RAG), and scenarios requiring more developer control
    • Design practical end-user experiences where AI shows up inside real work (e.g., Teams, SharePoint, web apps, business workflows).
    • Ship v1 quickly, then iterate based on user feedback and observed outcomes.

2) Own execution and client momentum (Primary)

  • Lead execution engagements following workshops and/or Copilot training.
  • Run working sessions with business + IT stakeholders to clarify scope, define success metrics, and unblock dependencies.
  • Operate with disciplined delivery hygiene:
    • Limit work-in-progress to protect focus and quality
    • Define clear milestones and decision gates
    • Show visible progress each month (working artifact or a confident “stop / not worth it” decision)

3) Apply “minimum viable guardrails” while moving fast (Primary)

  • Recognize common risk areas and readiness issues (oversharing, unclear data boundaries, unsafe tool usage patterns).
  • Embed practical guardrails into delivery without slowing the business down.
  • Escalate to senior advisors when complexity, risk, or architecture warrants it.

4) Identify and help execute data opportunities (Secondary)

  • Identify data gaps that block AI outcomes (availability, structure, access, ownership, quality).
  • Recommend pragmatic fixes that improve readiness and reliability.
  • Where appropriate, help execute lightweight data work such as:
    • Organizing and structuring content for retrieval (SharePoint/Teams structure)
    • Basic data extraction/feeds (exports, connectors, structured inputs)
    • Light transformation/cleanup steps
    • Coordinating with client IT/data teams or vendors when deeper work is needed

This is not a pure data engineering role, but you should be comfortable diagnosing data readiness issues and driving practical remediation to enable delivery.

5) Create repeatable delivery patterns and assets (Primary)

  • Capture reusable patterns, templates, and checklists:
    • Test scripts / evaluation sets
    • Rollout and adoption playbooks
    • Prompt patterns and agent design conventions
    • Document learnings and continuously improve how we deliver post‑workshop outcomes.
Qualifications
Must Have

Experience:

Proven experience delivering end-to-end client solutions (consulting, implementation, solutions engineering, product delivery, or similar).

  • Hands‑on capability in at least two of the…
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