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 build with AI as a core delivery tool — Anthropic's Claude powers our discovery synthesis, analysis & design generation, and prompt engineering workflows, and this role sits at the center of that capability.

What makes a star at Greenlight?

Inherently inquisitive and driven — you want to know how everything works
Fluent in both business and technology — you translate between worlds effortlessly
Passionate about AI and automation — you stay ahead of the curve because it excites you
Outstanding communicator — in the room with a CFO or a developer, you are equally at home
Ownership mindset — you do not wait to be told; you move

The Role

The Automation Business Consultant is Greenlight’s connective tissue between client problems and automation solutions - equal parts discovery expert, AI prompt architect, pre-sales solutions contributor, and delivery quality anchor. You will work directly with clients in their environments - facilitating process workshops, uncovering automation opportunities, shaping AI agent logic, and producing the specifications that our RPA engineers and FDEs build against.

You will contribute to solution design and proposals, and own a disproportionate share of what determines whether a project is scoped correctly and delivers on time.

This role is purpose-built for someone mid-career who is technically sharp, client-confident, and ready to operate at the intersection of automation delivery and AI consulting. As the Automation Business Consultant, you are accountable for 2-3 engagements at a given time and are the intelligence and documentation layer that makes the rest of the pod move.

At a Glance

Reports To

Director, Project Delivery & Strategy

Works Closely With

RPA Engineers, FDEs, Solution Architects, Pre-Sales

Client Interaction

Executive sponsors, process SMEs, solution stakeholders, vendor partners

Travel Requirement

Regular client travel required (onsite discovery, workshops, pre-sales)

Platform Focus

UiPath, Microsoft Power Platform, Anthropic Claude / AI Agents

Seniority

Mid-Career (4–8 years relevant experience)

Location

Onshore Canada — Toronto preferred

Engagement Type

Hybrid — onsite client-facing with occasional travel.

What You'll Do

Delivery Accountabilities

Partner with the PM for client readiness activities pre project kickoff
Lead process discovery workshops (onsite where required) running small builds in a single session and medium-to-large builds across three sessions with 1day Ai enabled PDD turnaround
Produce PDDs within one day of completing discovery — the authoritative build specification, zero ambiguity, zero gaps
Validate and refine AI-generated SDDs: accuracy against the PDD, security and scalability considerations confirmed and finalized with the engineer before build begins — without letting documentation block development
Facilitate sprint demos with the client at the close of each Build sprint
Begin test case development with the client in Week 1 of the build phase — in parallel with development, not after it — so UAT is ready to execute the moment a build is promotable
Coordinate UAT execution: prepare client testers, manage the schedule, triage defects, and drive closure before go-live
Where required, lead DU training during enablement and generate SOPs and change management materials where needed to close the gap between what was built and how the business will run it

AI Design & Prompt Engineering

Translate business problems into prompt architecture for Claude and other LLM components - defining reasoning logic, decision trees, fallback paths, output format specifications, and human-in-the-loop triggers