Account Executive - Enterprise Technology

The company

WORK-SELF builds Maya Enterprise, the Human Context infrastructure for enterprise AI. AI agents increasingly perform work inside large organisations, but they need context about the people and decisions around that work: who owns it, which decisions an agent can make, when a person reviews the result and how exceptions are escalated. Maya provides this governed context through an MCP-native server so employees and agents can complete work through accountable workflows.

We are a London-based enterprise AI company. In our first year we won enterprise contracts across banking, utilities, property services and European public finance, reached EBITDA-positive operations with a small team, and deployed Maya with enterprise customers. We work with technology and delivery partners and hold a granted US AI patent assigned to the company. Our next stage is scaling product delivery, enterprise adoption and partner-led growth. The people joining now will help build that next stage.

The role

You will build and close enterprise technology opportunities where agents, application platforms and data infrastructure need dependable human ownership, review and escalation. You own the commercial relationship with the enterprise customer, while also developing opportunities alongside integration and technology partners. You can explain how Maya complements systems of record, integration tools, data context platforms and AI governance products without positioning them all as direct competitors.
Expected compensation ranges: £75k – £140k per year (OTE) + £130k - £200k (Equity) + Bonus on performance and standard benefits

What you will own

• Enterprise technology accounts. Prioritise organisations adopting agentic workflows. Map CIO/CTO teams, AI platform owners, data leaders, business sponsors and delivery partners, with a clear accountable customer buyer for each opportunity.

• Technical discovery and solution selling. Diagnose context, permissions, escalation, review and workflow issues rather than selling AI features in isolation. Coordinate demonstrations and technical validation with product and engineering.

• Partner-enabled selling. Develop opportunities with systems integrators, enterprise application providers and relevant data or governance platforms. Define each party's role in a sale or deployment; do not confuse an ecosystem relationship with a signed customer commitment.

• Commercial execution. Develop executive proposals, pilot scopes, success measures and contracts. Navigate architecture review, security assessment, procurement and negotiation across long sales cycles.

• Market and technical intelligence. Capture objections and alternatives across workflow platforms, context infrastructure, orchestration and governance tools. Feed verified distinctions and practical integration questions to sales enablement, product and product marketing.

You will know you are succeeding when

• First 30 days. You understand how Maya fits into enterprise technology environments, have mapped priority accounts and their buying groups, and book or advance 5 qualified demos per week towards pilots or commercial evaluation.

• By 90 days. You book 10 qualified demos per week and convert 3 client pilots per quarter. Your named-account pipeline has clear buyer sponsorship, evidence-based value cases and documented next steps.

• By 180 days. You have closed £500k in new revenue, supported by signed agreements, credible value cases and reliable forecasts. You have documented lessons that make the sales approach repeatable.

Who does well here

• Track record selling complex enterprise software, data platforms, workflow automation, integration or AI-related infrastructure.

• Comfort selling to business owners and technical evaluators in the same opportunity.

• Experience running discovery, pilots, security or procurement processes and multi-party negotiations.

• Strong account-based prospecting and CRM discipline; forecasts grounded in customer actions.

• Ability to understand APIs, enterprise applications and agentic architectures without pretending to be the implementing engineer.

Success requires technical discovery, complex stakeholder alignment and accountable customer relationships, not broad outreach alone.

What you get

• A direct role in bringing a new layer of enterprise AI infrastructure to market.

• Close collaboration with product, engineering and technology partners.

• The opportunity to help build and scale an early-stage enterprise AI company.

If this resonates with you, we would love to hear from you. Any questions, please email us at careers@work-self.com