AI Lead Consultant, AI Practice

AI Lead Consultant

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 are 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 - and this role sits at the center of how we define what gets built and why it matters.

What makes a star at Greenlight?

• Thinks like a consultant, challenges like someone who has seen AI projects fail
• Embedded onsite - you live in the client's world until the solution works
• AI-fluent without being an engineer - you know what agents can and can't do
• Builds the business case before recommending the solution
• Iterative by instinct - you refine as you learn, not after the project closes

The Role

The AI Lead Consultant is the analytical and commercial intelligence layer of every Greenlight AI engagement. You are not an engineer - you are the person who ensures the engineer builds the right thing. Working in a two-in-a-box model alongside a Forward Deployed Engineer, you own the what and the why of every automation initiative: why this process, what it should do, how success is measured, and what the business must look like after the AI agent is deployed.

The most common failure mode in enterprise AI is not a technology failure - it is a requirements failure. Engineers build exactly what they are asked to build, and what they are asked to build is often a precise replication of a broken process, wrapped in AI. The result: faster execution of the wrong workflow. This role is the structural safeguard against that outcome.

You will travel to client sites, run discovery workshops, challenge process assumptions, design the business logic that AI agents will execute, build the business case that justifies investment, and produce the requirements that the FDE builds against without ambiguity. You are client-facing, delivery-accountable, and commercially aware. This is not a back-office BA role.

At a Glance

Reports To

Practice Lead

Works Closely With

Forward Deployed Engineer, Pre-Sales SE, AI Delivery Engagement Manager

Client Interaction

Yes - C-suite, operations leaders, process owners, compliance stakeholders

Travel Requirement

Regular client travel required - discovery, workshops, executive readouts

Platform Focus

Anthropic Claude (Cowork + Skills), Claude.ai, AI agent frameworks

Seniority

Mid-to-Senior (3-5 years relevant experience)

Location

Onshore Canada - GTA preferred

Engagement Type

Hybrid - embedded onsite client delivery with remote phases

The Two-in-a-Box Model

Every AI Practice engagement runs with two people who together form a complete delivery unit:

Role

Owns

How They Work Together

AI Lead Consultant ★

The what and why - process discovery, requirements, business case, stakeholder management, AI logic design

You define what gets built and why. The FDE builds it.

Forward Deployed Engineer

The how - AI agent architecture, build, MCP integration, production deployment

Translates your requirements into working software. Flags what is technically feasible before you commit to a client.

What You'll Do

Process Discovery and Reengineering

• Lead structured current-state process discovery sessions with client stakeholders - walkthroughs, observation, value stream mapping - at a fidelity that captures decision logic, exception handling, system touchpoints, and handoff points
• Challenge the client's stated requirements with disciplined questioning: if a process step exists because 'we've always done it this way,' surface and resolve that before it becomes an automation constraint
• Apply Lean thinking to identify waste, bottlenecks, and manual interventions that should be eliminated - not automated - before an agent is built
• Produce redesigned future-state process maps that represent the optimized workflow the AI agent should execute, not the legacy process with AI layered on top
• Facilitate executive-level process reviews to validate redesigned workflows and secure stakeholder alignment before build begins

AI Agent Requirements and Logic Design

• Author clear, complete, and structured requirements for AI agent automations - written precisely enough for engineering and accessibly enough for business sign-off
• Define agent decision logic in structured formats: