HybridFull Time

Salary

$75 - $90 / hr

Location

Toronto, ON

Posted

Aug 24, 2026

Role overview

Job Title: RPA Architect AI

Location: Toronto, ON Hybrid (2-3 Days onsite) Mode: Contract

(Not QA profile, 10+ years needed)

Job Description:

  • Design end-to-end automation solutions using UiPath Studio, Orchestrator, and related components, ensuring scalability, security, and reusability.
  • Define and enforce automation architecture standards, coding guidelines, and best practices (REFramework, modular design, exception handling).
  • Conduct solution design assessments, feasibility studies, and technical design documents (TDD/SDD) for complex automation initiatives.
  • Architect and implement AI-infused automations using UiPath AI Center, Document Understanding, and Communications Mining to handle unstructured/semi-structured data.
  • Integrate Generative AI and LLMs (UiPath GenAI Activities, Azure OpenAI, Anthropic Claude, or similar) into automation workflows for tasks such as summarization, classification, extraction, and decision support.
  • Design and deploy UiPath Autopilot / AI Agents and agentic automation patterns for dynamic, context-aware process execution.
  • Evaluate, train, and fine-tune ML models within AI Center; manage model lifecycle (training, validation, deployment, retraining).
  • Identify automation candidates that benefit from AI/ML augmentation (e.g., cognitive document processing, intelligent triage, predictive routing) and lead their technical design.
  • Ensure automation solutions comply with security, data privacy, and IT governance standards (role-based access, credential/asset management, PII handling in AI workflows).
  • Partner with business stakeholders to translate process requirements into scalable automation and AI solutions.
  • Lead architecture review boards, code reviews, and quality assurance for automation deliverables.
  • Collaborate with data science and AI teams to align automation strategy with broader enterprise AI initiatives.
  • Establish reusable component libraries, AI model repositories, and automation frameworks.
  • Monitor solution performance, AI model accuracy/drift, and automation ROI; recommend optimizations.
  • Stay current with UiPath platform updates, AI/GenAI advancements, and industry best practices; evaluate new features for enterprise adoption.