AI Engagement Manager

Greenlight Consulting•Toronto, ON

8d•CA$110,000 - CA$130,000•Hybrid

About The Position

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 run AI-assisted delivery as standard — from discovery through documentation to quality assurance — and this role operates in an environment where Anthropic's Claude is embedded in how we work, not treated as a future state. The AI Engagement Manager is the accountability seat on every Greenlight engagement. This role owns the client relationship, the commercial track, and the delivery cadence across AI and automation projects running on tight, high-output cycles. Engagements operate with an AI Automation Business Consultant who owns process discovery, AI logic design, and technical specification, alongside an Automation Engineer who owns the build. This role sits above both, as the commercial and relational anchor keeping the engagement on track, the client confident, and the value visible. This means the role requires AI and automation fluency to have credible conversations about what is being built, how long it should take, and what the business outcome looks like - without needing the consultant or engineer to translate. You do not build the solution, but you need to understand it well enough to own it in the room.

Requirements

5–8 years in a delivery, project management, or client-facing consulting role within technology — automation, AI, and RPA, development projects strongly preferred
Proven track record managing short-cycle software delivery projects (3–6 months) end-to-end across a portfolio of concurrent engagements
Direct exposure to AI or automation project delivery — you understand what an LLM-based agent or RPA build involves, the risks that are specific to these projects, and how to talk about them with a client
Experience managing executive and senior stakeholder relationships, including difficult commercial conversations and expectation management
Background in a consulting, managed services with experience delivering AI or agentic automation projects
Track record of managing scope, change orders, and commercial conversations in a professional services or managed services environment
Agile delivery — sprint planning, backlog prioritisation, retrospectives; comfortable running a cadence without a dedicated Scrum Master
Commercial acumen — you understand project economics, margin, scope risk, and value-based delivery; you protect both the client relationship and Greenlight’s position
AI and automation fluency — not an engineer, but not a stranger to the technology; you can discuss agent logic, LLM outputs, and automation architecture with enough credibility to hold a client room
Political intelligence — you read room dynamics, sense wavering confidence, and act before issues escalate
Clear, confident communicator — you simplify complex technical and commercial topics without losing accuracy for either audience
Calm under pressure — you de-escalate, not amplify; clients feel steadier after talking to you, not more anxious
Organised across complexity — managing 3–4 concurrent projects with different clients, consultants, and build stages without losing thread

Responsibilities

Serve as the primary client contact from kickoff through post-delivery review — the face of the engagement at every stage
Build trusted relationships with executive sponsors, business leads, and key stakeholders — read the room, sense shifts in confidence, and act before they become delivery risks
Own scope management and change control — identify creep early, facilitate commercial conversations around change with clarity, and protect margin without damaging the relationship
Lead difficult conversations: expectation resets, delay communications, scope disputes — directly, calmly, and without escalating to leadership by default
Maintain commercial awareness across your portfolio; flag risks to margin, timeline, or client satisfaction before they surface in a steering call
Understand what is being built on every engagement — the automation logic, the AI agent behaviour, the integration points — well enough to represent it accurately to a client executive
Track delivery against the Week 0-to-go-live cadence: readiness confirmed before kickoff, discovery completed in Week 1, SDD validated before build, test cases parallel with Sprint 1
Identify when the delivery thread is at risk — slow discovery, SDD delays, build blockers, UAT gaps — and drive resolution before it affects the timeline
Maintain the RAID log, delivery schedule, and status reporting to client stakeholders — governance that is current, honest, and actionable, not performative
Manage competing priorities across 3–4 concurrent engagements without losing thread or dropping client confidence
Create the conditions for the Automation Business Consultant and Engineer(s) to do their best work: resolved blockers, aligned stakeholders, clear scope, no unnecessary noise
Review Consultant deliverables — PDDs, agent specs, SDD sign-offs — for quality and alignment with client expectations before they go to the client
Coach the consultant through complex stakeholder sessions and difficult discovery conversations — your experience in the room is a resource, not a rank
Manage resource capacity and flag resourcing risks early — don’t let a build slow down because of a preventable resource constraint
Proactively track whether clients are realizing expected automation and AI value — report against agreed KPIs and business outcomes, not just delivery milestones
Lead go-live communications, confidence management, and post-delivery debriefs — close every engagement with the client seeing what was delivered and what it was worth
Identify expansion opportunities within active accounts — process gaps, adjacent automation candidates, AI use cases the client hasn’t named yet — and surface them to the account team