Applied AI Partnerships & Mid-Market Growth Lead

monō ai; Sydney; Vor Ort; AUD 120.000 - 180.000; Vollzeit

Zusammenfassung

monō ai in Sydney seeks a client‑facing Growth & Partnerships leader to scale existing SI and hyperscaler partnerships, originate mid‑market business, and own the commercial path from first conversation to contract. You will work across clients and partner teams, crafting economic models that prove measurable value and drive revenue.

You will leverage your experience selling platform and services, align frontline workflow with executive priorities, and navigate complex stakeholder conversations

Qualifikationen

• Must have built and closed B2B pipeline with partner-influenced opportunities.
• Ability to learn unfamiliar business quickly and connect priorities to economics.
• Substantive fluency in applied AI and enterprise tech, including LLMs, APIs and data lineage.

Aufgaben

• Scale partner-to-client growth with SI and hyperscaler alliances.
• Originate and close mid-market business directly and with partners.
• Identify real operating constraints and deliver measurable value.
• Shape focused first deployment with Deployment Strategists and engineers.

Kenntnisse

B2B sales

Partnership development

Pricing strategy

Account management

Executive stakeholder skill

Outcomes-based thinking

Jobbeschreibung

monō ai | Sydney, Australia | Full-time | Client-facing, with regular in-person work at our Sydney office, customer and partner sites

Scale partnerships already in place. Win the work. Prove the outcome-and know how to price for it.

monō ai is leading the Applied AI category in Australia. Our GTM team, Deployment Strategists and Forward Deployed Engineers (FDEs) are leading the next wave of AI customer transformation: getting inside how businesses operate, deploying capability into real workflows and proving measurable outcomes. We are a platform company with a distinctive blend of consultancy and professional services. We are not just one of those things-we bring all three together to make AI work in production. Experience with outcomes-based pricing is a real advantage here: for example, framing an Applied AI deployment around a measurable reduction in the cost of processing a mortgage loan application, rather than selling only software seats or delivery days.

Most enterprises are not short of AI ideas. They struggle to identify where AI will materially improve the business, redesign the work around it and bring the change into production. Our enterprise AI orchestration platform and small, senior pods of operators and engineers work inside clients’ businesses, on their infrastructure, to prove value through working capability-not roadmaps alone.

Our partnerships with systems integrators (SIs) and hyperscalers are already in place. Your job is to scale them into active co‑selling, qualified opportunities and revenue, while originating and closing mid‑market business yourself. You will turn established partner access into client outcomes and personally own the commercial path from first conversation to contract and evidence‑led expansion. This is a hybrid partnerships and direct‑sales role, not a mandate to sign partnerships from scratch, partner marketing or a relationship‑management post.

What you will own

• Scale an existing partner‑to‑client growth motion. Activate and deepen our established SI and hyperscaler partnerships with account teams, specialists and alliance leaders. Identify where our platform and embedded delivery model complement their priorities; build joint account plans, enable co‑selling and convert partner engagement into qualified opportunities and closed revenue. Maintain clear ownership of the client relationship, economics and next steps.
• Originate and close mid‑market business. Prospect directly as well as through partners. Form a point of view on each account, reach economic buyers and operating leaders, run a disciplined sales cycle and negotiate platform and services agreements. Build the motion from 0→1 rather than waiting for inbound demand or a finished playbook.
• Find the real operating constraint. Go beyond the executive brief to observe how work happens across frontline teams, systems, handoffs, exceptions, decisions and controls. Identify the narrowest valuable problem to solve-and when the real answer is workflow or operating‑model change rather than more AI.
• Make the economics defensible. Establish a baseline and distinguish cash saved, capacity released, revenue created, losses avoided and risk reduced. Build a value case that names assumptions, owners, dependencies and the evidence required before anyone claims success. Where appropriate, explore outcomes‑based commercials with a clear metric, measurement method and shared understanding of what drives the result.
• Shape a focused first deployment. Work closely with our Deployment Strategists and Forward Deployed Engineers to assess feasibility, data access, security, human oversight and adoption. Align the client and any partner on a bounded first use case, delivery scope, platform and professional‑services commercials, decision‑makers and a credible route to production. Do not commit engineering to what has not been validated.
• Stay accountable beyond signature. Maintain executive and partner alignment while the delivery pod leads implementation. Check whether the capability is used, the workflow has changed and the promised value is real; use that evidence to earn the next opportunity, or recommend a pivot or stop.
• Create leverage. Feed field insight into our platform, delivery methods and GTM playbook. Keep partner attribution, account ownership, pipeline and forecasts clear and honest.

What you bring

• Evidence that you have personally built and closed B2B pipeline and made partner‑sourced or partner‑influenced opportunities commercial, not just signed alliances or managed introductions.
• The ability to learn an unfamiliar business quickly, challenge the stated request respectfully and connect executive priorities to frontline workflow and credible economics.
• Substantive applied‑AI and enterprise‑technology fluency. You can discuss LLMs and agents, APIs, integrations, data lineage, evaluation, security, latency, cost, auditability and human control with technical stakeholders. You know when to go deep and when to bring in an engineer; you do not bluff.
• Comfort selling a platform and the scoped implementation or transformation work required to create value, with sound judgment about what is feasible, measurable and worth buying.
• High energy, consistent prospecting and 0→1 startup experience. You can create structure under ambiguity, handle difficult stakeholder conversations and remain accountable when an initial thesis fails.

Helpful, not required

experience structuring outcomes-based pricing; consulting or operating experience; professional‑services selling or scoping; work with SIs or hyperscaler co‑sell motions; exposure to regulated or workflow‑heavy sectors.

Why monō?

The opportunity is to build a repeatable commercial engine without losing the truth of what works in production. Outcomes‑based pricing experience will be a real benefit: think of a mortgage broker measuring and, where commercially appropriate, pricing against a verified reduction in loan‑application processing cost-not simply AI usage or hours delivered.

Tell us about a partner‑led opportunity you helped turn into revenue, a client workflow you investigated beyond the initial brief, and-if relevant-how you structured pricing around a measurable customer outcome.

If you can show the capabilities above but do not match every preferred background, we encourage you to apply. We are committed to a fair and inclusive hiring process.