Friends, we are at 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.

• 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

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

• 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.

• 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:
  • Maintain a prioritized delivery backlog
  • 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)

• 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.

• 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.

• Capture reusable patterns, templates, and checklists:
  • “Ready” criteria
  • 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.

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 following areas:
  • Building AI assistants/agents (Copilot Studio preferred, or similar platforms)
  • Power Platform delivery (Power Automate; Power Apps is a plus)
  • M365 delivery experience (SharePoint/Teams; permissions and sharing concepts)
  • API-based integration experience (basic to intermediate)
  • Data estate modernization and enablement
• Strong execution judgment:
  • Ships v1 quickly, iterates often
  • Manages scope and expectations
  • Closes loops and shows outcomes
• Strong communication skills with business stakeholders (clear, pragmatic, non-jargony).

• Familiarity with Azure building blocks (Functions, Logic Apps, storage, identity patterns).
• Familiarity with Azure-based AI patterns and when they’re appropriate (Azure OpenAI / Azure AI Foundry).
• Light a