Join us
Building the team that builds the ADO.
The first hires set the technical foundations, the culture, and the trajectory. We're not filling roles — we're picking the people who'll still be here when the ADO is running inside hundreds of enterprises.
Where we're hiring
The disciplines we're building across.
Not a fixed headcount. If your experience spans more than one area, that's a strength. If you don't tick every box on a list, apply anyway — the right mindset and the right experience rarely come pre-packaged.
Principal Agentic Architect
The most important technical hire we make. You'll design and build the Multi-Agent System at the heart of the ADO — orchestration, agent communication, execution pipeline. A production system running on live enterprise data, reliable at 3am when nobody's watching. We have strong views on the answer. We need someone who can execute on them and isn't afraid to challenge them. The architecture decisions you make now will be running inside Fortune 2000 data teams within eighteen months.
We're an AI-native organisation. Work where you do your best thinking.
Semantic Engineer
The Unified Semantic Layer is the Rosetta Stone between fragmented enterprise data and the agents that query it — business logic, metric definitions, and lineage, all encoded so agents can interrogate reliably and governance can audit adversarially. You've done schema archaeology on real enterprise estates — the kind where "revenue" means six different things across eight systems. Get it right and the ADO works. Get it wrong and it hallucinates. The quality bar is not negotiable.
Not a junior position. Principal-level role with founder-level influence on the core product.
ML Engineer
The ADO's execution layer includes specialised predictive agents — demand forecasting, churn modelling, propensity scoring, anomaly detection. Your job is to build them and keep them honest. The interesting challenge isn't the modelling — it's the integration. A prediction inside an agentic workflow has to be explainable to the adversarial governance layer, consumable by the narrative agent, and auditable by the client. If you've spent your career making models production-ready and you're tired of being treated as an afterthought, this is built differently.
Production ML experience matters more than academic background. We care about what you've shipped, not where you studied.
AI Enablement Consultant
The consulting practice is the front door — where we establish trust, diagnose the structural problems, and design the foundations the ADO runs on. You'll work directly with C-suite stakeholders, helping them understand what's actually blocking their AI programmes. Not a traditional consulting role: the diagnostic work you do becomes the blueprint for a technical build. You need genuine command of the data and AI landscape — credible enough to discuss semantic layers and agent architecture with a CDO, commercial enough to frame it as a business problem.
You don't need to write code — but you need to understand what the code does and why it matters.
Enterprise Sales Lead
You're selling a category that doesn't have a name yet. Kallidin fixes the structural data problem behind most enterprise AI failures — your job is to help C-suite executives see it before they've named it themselves. The model is diagnostic-first: the opening engagement is a fixed-fee assessment, not a product pitch. You win by being right, not by being persistent. You'll be in the room with a CDAO talking architecture and a CFO talking payback period.
Category-creation role. You'll help define how Kallidin goes to market, not just execute a playbook someone else wrote.
Sound like you?
No formal application process. Send a note — who you are, what you've built, and why Kallidin.
Not looking for a job?
If Kallidin sounds like the answer to a problem you're dealing with, start with a diagnostic.
Start with a Diagnostic