GTM Engineer / Ai Revenue Architect

Senior GTM Systems Engineer Remote / London hybrid / United Kingdom / New York upto £135k / USD $190k Not a Salesforce admin. Not a traditional RevOps analyst. You're the person building the technical infrastructure behind the revenue engine. We're looking for someone who sees a manual process and asks, why does a human need to do this?

Someone who looks at a Salesforce workflow and thinks about what happens when the business doubles. Someone who has moved beyond experimenting with AI and is already building agents, automations and data-driven workflows into their GTM stack. This is a hands-on technical role at the intersection of RevOps, GTM Engineering, data architecture and AI.

You won't simply administer the existing stack. You'll help decide what the stack should look like, build the infrastructure underneath it, and continuously improve how revenue teams operate.

What you'll actually be doing

Systems architecture

• Own the design and build of our GTM systems architecture, including the data layer, orchestration logic and the tools revenue teams interact with.
• Audit the existing GTM stack and identify where complexity, duplication or unnecessary integrations are creating friction.
• Design systems for composability, so capabilities can be reused, tested and replaced without rebuilding the entire architecture.
• Think beyond Salesforce as the centre of the architecture. Salesforce remains an important system, but shouldn't be the only place where business logic or data lives.
• Build systems that can scale with the business rather than requiring a rebuild every 18 months.

AI deployment

• Build and deploy AI-assisted workflows across the revenue cycle, including lead qualification, deal intelligence, outreach, renewal signals and forecasting inputs.
• Move beyond prompting. Connect models to live business data, trigger them through events and workflows, and systematically evaluate their outputs.
• Build and deploy AI agents and automations that solve genuine operational problems.
• Use APIs and orchestration tools to integrate AI into existing GTM workflows.
• Identify opportunities where AI can remove repetitive operational work and materially improve the efficiency of revenue teams.
• Measure the impact of what you build rather than simply deploying technology because it's new.

Data and integrations

• Design and maintain the underlying data model for accounts, contacts, opportunities, pipeline stages and revenue events.
• Establish clear definitions of truth across CRM, marketing automation, data warehouse, enrichment and AI tooling.
• Own the health and reliability of integrations across the GTM stack.
• Work with APIs, webhooks and data pipelines to connect systems together.
• Build observability into workflows so failures, data issues and broken integrations are identified before they become business problems.
• Bring strong data governance instincts and challenge poor data architecture when you see it.

RevOps enablement

• Translate strategic GTM decisions into working systems. For example, a new segment shouldn't just mean a new Salesforce field. It could mean new routing logic, qualification criteria, stage gates, automation and reporting.
• Partner with Sales, Marketing, Customer Success and Partnerships to understand how the entire revenue engine should operate.
• Instrument the full bow tie, not just the pre-sale funnel.
• Be the person who asks, "Before we add another tool, what can our existing architecture already do?"
• Work closely with GTM leadership to turn business requirements into scalable technical solutions.

What we're looking for

Must haves

• 5+ years of experience across RevOps, GTM Engineering, Revenue Systems or a closely related function.
• Strong hands-on Salesforce experience. You understand the platform deeply, but you've grown well beyond traditional administration.
• Demonstrable experience building and deploying AI into production workflows. We're looking for actual implementations, not just experimentation.
• Strong data modelling instincts. You think about schemas, relationships, ownership and data flows before configuring fields.
• Experience working with APIs, webhooks and integrations.
• Experience with automation/orchestration platforms such as Clay, n8n, Make, Zapier or comparable tooling.
• Strong understanding of how modern GTM systems should be architected and integrated.
• Ability to move between strategic thinking — "here's what the business needs" — and technical execution — "here's how I'm going to build it."
• Comfortable working in an environment where you'll need to figure things out rather than follow an established playbook.

Strong signals we'll look for

• You've replaced manual SDR, sales or operations workflows with AI-augmented systems and can demonstrate the impact.
• You've built on top of LLMs using APIs rather than simply using ChatGPT.
• You've built or deployed AI agents that can access data, make decisions and trigger actions.
• You have experience with SQL, Python, JavaScript or similar technologies that allow you to go beyond no-code tooling when required.
• You've worked with data warehouses, reverse ETL, enrichment platforms or event-driven architectures.
• You've built complex workflows using Clay, n8n, Make, Zapier or similar tools.
• You've formed strong opinions about data governance and can explain why you made those decisions.
• You've worked in a scale-up where systems had to evolve rapidly alongside the business.
• You've experienced the pain of a GTM stack becoming too complex and have subsequently simplified it.
• You've built systems that other people can actually operate, monitor and understand.

What this role is not

• It's not a Salesforce admin role with a more interesting title.
• It's not a traditional RevOps analyst role focused primarily on reporting and dashboards.
• It's not a project manager coordinating technical work that other people execute.
• It's not an integrations-maintenance role where your job is simply keeping existing workflows running.
• It's not a role for someone whose AI experience is limited to writing better prompts in ChatGPT.
• It's not about buying another tool every time a process becomes difficult.

The architecture we're building

We're moving towards a headless revenue architecture where the data, business logic and user-facing tools are increasingly decoupled. The goal is for the underlying revenue data and business logic to be accessible across the GTM ecosystem rather than being trapped inside a single platform. For example, account and opportunity data shouldn't only exist inside Salesforce.

The underlying data and logic should be accessible to other systems, workflows and AI agents through APIs and orchestration layers. That means: AI agents can act on reliable, governed data.

• Sales and marketing tools can compose on top of shared infrastructure.
• Business logic can be reused across multiple workflows.
• New tools can be introduced without rebuilding the entire stack.
• Systems are observable, testable and measurable.
• The architecture can evolve as the business grows.

This person will be a key technical builder in that evolution. Not the manager of it. Not the vendor of it. The builder. If you've been waiting for a company that will actually give you the scope to build the GTM systems you've been sketching out on whiteboards, this is that role.