Automation Engineer (RPA)

Job description

Greenlight helps organizations solve complex business challenges through intelligent automation, agentic AI, and custom technology solutions. Our teams work directly with clients to understand their operations, identify opportunities, and rapidly build solutions that create measurable business value. We combine deep consulting expertise with hands-on engineering to bridge the gap between strategy and execution. We’re building a future where consultants and engineers work alongside AI to deliver faster outcomes, stronger businesses, and transformative customer experiences. We use Anthropic’s Claude as a core delivery tool — embedded in how we design, build, and validate automation solutions — and this role works inside that environment every day. **What makes a star at Greenlight?** - Builder’s mindset: you are not satisfied until it works cleanly, scales properly, and is reusable - Technically restless: you stay current because the space moves fast and you want to be ahead of it - Hands-on with AI — you have worked with LLMs, understand their behaviour, and think about how to build against them, not just with them - Clear communicator — able to explain what you built, why it works, and what it can and can’t won’t do - Ownership mindset — the build is yours until it’s live and stable **The Role** The AI Automation Engineer is the execution layer of the Greenlight delivery pod. You take a signed-off Solution Design Document and build it — cleanly, efficiently, and to a standard that holds up in production. This is not a back-office development role. You work in a lean, fast-moving pod alongside an Automation Business Consultant who owns the process logic and AI specifications, and an AI Delivery Engagement Manager who owns the client. Your job is to build what the spec says, flag what the spec missed, and close the gap between design and working software in two-week sprints. As AI agent-based automation grows alongside our RPA practice, this role requires genuine fluency across both — UiPath for process automation, and Claude-powered agent workflows for AI-driven solutions. The developer who thrives here is comfortable in both worlds and curious about where they intersect. **At a Glance** | | | | --- | --- | | **Works Closely With** | AI Engagement Manager, Automation Business Consultant, RPA Engineers | | **Client Interaction** | UAT support, technical walkthroughs, go-live | | **Platform Focus** | UiPath, Anthropic Claude / AI Agents, Microsoft Power Platform etc. | | **Seniority** | Mid-to-Senior (3–7 years relevant experience) | | **Location** | Onshore Canada — GTA preferred | | **Engagement Type** | Hybrid Office — project-based with occasional client-site phases | **What You’ll Do** **Build, Configuration & Test** - Build automation solutions in UiPath Studio — efficient, well-structured, maintainable, and easy to understand; scalability and reusability are not optional - Develop and configure AI agent workflows using Anthropic’s Claude API — integrating LLM components, defining tool use, and wiring agent logic into the broader automation pipeline - Validate the AI-generated SDD before build begins — flag gaps, ambiguities, or technical constraints early rather than discovering them mid-sprint - Create reusable components, activity libraries, assets, and queues via UiPath Orchestrator and Studio that raise the quality floor for every subsequent build - Integrate automations with client systems — ERPs, CRMs, document management platforms, APIs — handling authentication, error handling, and edge cases the spec may not have fully anticipated - Write and execute unit tests and pre-UAT test procedures — own quality before the client ever sees it - Participate in UAT alongside the Automation Business Consultant — triaging defects, distinguishing genuine build issues from scope queries, and resolving assigned bugs with speed and quality **AI Agent Development** - Build and configure agents: define tool schemas, implement decision logic, handle multi-turn interactions, and ensure output formats match the finalized specifications - Implement prompt-to-output pipelines — taking the prompt architecture designed and wiring it into the automation workflow reliably and testably - Handle LLM failure modes in code: output validation, fallback logic, human-in-the-loop triggers, and graceful degradation when model outputs fall outside acceptable parameters - Work with JSON editors, API testing tools, and the Anthropic API directly to validate agent behaviour before it reaches a client environment **Procode & Custom Development** - Write Python, JavaScript, or C# beyond the UiPath activity layer — custom scripts, data transformation pipelines, lightweight utilities, and integration logic that the platform can’t handle natively - Build and maintain custom UiPath activities and libraries where out-of-the-box components fall short — extending the platform rather than working around it - Design and implement API integrations from scratch where no connector exists — authentication, payload handling, retry logic, error management, and response parsing - Develop lightweight back-end components to support agent workflows — data preprocessing, output formatting, webhook handlers, and queue management that sit outside the automation itself - Know when to build inside the platform and when to step outside it — that judgment is what separates an automation engineer from an RPA developer **Delivery Participation** - Work within two-week sprint cycles — sprint planning, daily standups, reviews, and retrospectives; you are an active participant, not a resource that takes tickets - Respond quickly to build-phase queries from the Automation Business Consultant — ambiguity in the spec costs sprint time; resolve it fast and document the outcome - Flag technical risks, integration blockers, and scope gaps to the AI Delivery Engagement Manager as they surface — not after they have already caused a delay - Support go-live and post-deployment stabilisation — the build is not done until the client is running it confidently in production **What We’re Looking For** **Essential Experience** - 3–7 years of hands-on development experience, with at least 2 years on UiPath a client-facing or professional services environment - Demonstrated experience building and deploying AI agent or LLM-integrated solutions — not just familiarity; you have shipped something that uses an LLM in production - Strong understanding of automation architecture: reusability patterns, exception handling frameworks, Orchestrator asset management, and queue-based processing - Experience working within agile delivery cycles — sprint-based development, backlog grooming, iterative releases - Direct exposure to enterprise system integrations — REST APIs, ERPs, CRMs, document platforms — and the real-world messiness that comes with them **Skills & Competencies** - Precision builder — your code is clean, documented, and maintainable; you do not leave technical debt for the next person to clean up - AI-native — you understand how LLMs behave, where they fail, and how to build against probabilistic outputs reliably - Problem-solver, not ticket-closer — when the spec is incomplete or the integration is messier than expected, you figure it out and document what you found - Fast and focused — two-week sprints are real; you scope your effort, hit your commits, and flag early when something is bigger than it looked **Technical Familiarity** - UiPath Studio, Orchestrator, REFramework — proficient across the full development and deployment lifecycle - Anthropic Claude API or OpenAI API — tool use, structured outputs, prompt chaining, output validation - REST API integration — authentication patterns, error handling, payload management - JSON, Python, or C# — enough to build custom activities, handle data transformations, and extend UiPath where needed - Postman or equivalent API testing tools for pre-integration validation - Jira or Azure DevOps for sprint tracking and defect management - Microsoft Power Platform (Power Automate, Power Apps) awareness is a plus **What Great Looks Like in This Role** | | | --- | | **The standard we are hiring to:** The best developers we have worked with treat the SDD as a starting point, not a ceiling. They build what it says, flag what it missed, and leave the codebase better than they found it. They know how an LLM will behave under pressure before they deploy it, because they tested it. They don’t wait for UAT to find the edge cases — they find them first. And when something breaks in production, they are already diagnosing it before anyone has to ask. | **Our Process & Practices** **Use of AI in Hiring** We use AI tools to support parts of our recruitment — things like organizing applications and flagging relevant experience. These tools inform our process, they don't drive it. Every hiring decision is made by our team, full stop. **Compensation** The expected salary range for this role is $110,000 – $140,000 CAD annually, based on experience and qualifications. We're happy to discuss further in conversation. **Equal Opportunity** Greenlight is an equal opportunity employer. We believe in fair consideration for all candidates regardless of ethnicity, culture, sexual orientation, religion, age, gender, or disability status. **Great Place to Work™** Greenlight is a certified Great Place to Work™ — 98% score. We're proud of that and we work every day to keep earning it.