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: decision trees, if-then-else flows, escalation triggers, exception routing rules, and confidence threshold criteria
• without writing code
• Document scope boundaries explicitly: what the agent handles autonomously, what requires human-in-the-loop review, and what must always be escalated
• Write user stories and acceptance criteria that specify the actor, the action, the business outcome, and the measurable success condition
• Identify and document edge cases, exceptions, and failure modes the agent must handle gracefully
• derived from discovery, not assumption
• Validate requirements completeness with the FDE before build begins
• no ambiguity that would require the engineer to make business decisions mid-build AI Platform Fluency
• Develop and maintain working fluency in Claude Cowork and the Skills framework as a power user
• understanding how Skills are structured, how agents reason through tasks, and how human-in-the-loop checkpoints are configured
• Use Claude directly during discovery and validation
• testing sample inputs against prototype agents to assess output quality before requirements are finalized
• Distinguish between process steps best suited to AI agent automation, workflow tools, or continued human execution
• and make that recommendation as part of the automation blueprint
• Understand prompt design principles conceptually
• sufficient to review and give feedback on agent logic without authoring the technical implementation
• Recognize when a requirement is technically unfeasible for the current platform and escalate early rather than allowing the FDE to discover it mid-build
• Stay current on Claude and broader AI platform updates
• translating new capabilities into automation opportunities for clients Business Case and ROI
• Build automation business cases that quantify: current-state cost, projected AI-enabled capacity, error reduction value, compliance risk mitigation, and net investment payback period
• Develop ROI models at conservative, base, and optimistic confidence levels
• tied to measurable process assumptions the client validates
• Prioritize automation opportunities by value-to-complexity ratio, enabling clients to sequence their AI roadmap by impact and feasibility
• Produce executive-ready business case presentations that translate quantitative ROI into strategic narrative
• what this automation enables the business to do that it could not before
• Track realized value post-deployment against projected figures
• closing the loop on delivery credibility Stakeholder Management and Client Advisory
• Design and facilitate discovery workshops, process reviews, requirements walkthroughs, and alignment sessions throughout the engagement lifecycle
• Translate AI concepts
• agents, Skills, prompt chains, human-in-the-loop design
• into language that resonates with operations managers, compliance officers, and C-suite audiences
• Manage competing stakeholder priorities and process ownership disputes that arise when automation redesigns cross departmental boundaries
• Serve as the day-to-day business-side point of contact for client stakeholders, protecting the FDE's focus time for engineering delivery
• Produce high-quality written deliverables: proc