AI Delivery Lead

Job Description

Anthrobyte wins by putting AI agents into the real operations of enterprise clients — and making them work, in production, where it matters. We need a leader who does this exceptionally themselves and builds a team that does it the same way. You'll own how we deliver AI, from the client's messy reality to a system they trust — and turn every engagement into repeatable capability inside our ATLAS platform.

This is a build-from-the-front role. You lead from inside the problem, not from a whiteboard.

What you'll own

— Embedded delivery. You go where the work is — inside the client's environment, alongside their operators — to understand their workflows, data, and constraints firsthand. You don't design in the abstract; you build against reality.

— End-to-end ownership. From business problem to architecture to a deployed, secured, monitored agent running in production and driving outcomes. You own the whole arc, not a slice of it.

— The product loop. You turn what you learn in the field into reusable patterns, accelerators, and standards inside ATLAS — so the next engagement is faster and we compound into a product, not a services treadmill.

— Building the team. This is the multiplier. You hire, mentor, and level up engineers to work the way you do — codifying your instinct into a playbook others can run. Success is a team who deliver like you, not you delivering everything.

— Client trust. You sit across from client security, data, and business leaders and earn their confidence — in architecture reviews, security reviews, and the room where decisions get made.

Requirements

What we're looking for

— Production track record. You've personally taken AI, ML, or agent systems into enterprise production — not demos, not pilots that died. You've worked with real client data and lived with what you shipped.

— Proof you build people. You've mentored engineers, set technical standards, and shaped how a team works. Evidence you can reproduce your skill in others, not just exercise it yourself.

— Range. You're equally comfortable writing code, whiteboarding architecture, and holding your own with a client's CISO.

— Systems mindset. You think in reliability and trust, not cleverness — enterprise AI is won on auditability and things that keep working.

— Ownership drive. You're energised by building something from early and shaping how it's done.

Bonus points

— Domain experience in supply chain, procurement, pricing, or other regulated / operational enterprise environments.

— Exposure to enterprise security, governance, and responsible-AI practices.

— A track record of growing engineers into strong, independent operators.

What this role is not

A back-office ML research lead. A pure people-manager who's left the tools behind. An architect who designs from a distance and never touches the client's reality. If you want to lead from a whiteboard rather than from inside the problem, this isn't it.

How we work

Small, high-trust, founder-close team. Governance-first, because our work runs inside clients' live operations. You'll have real authority over how we build and deliver — and the mandate to shape the team around strong talent.

How to apply

Tell us two stories. First: one AI system you took into enterprise production — the messy problem, how you built and secured it, and what broke along the way. Second: someone you made better — an engineer you grew, and how. Those two stories tell us more than any CV.

Requirements
What you'll own — Embedded delivery. You go where the work is — inside the client's environment, alongside their operators — to understand their workflows, data, and constraints firsthand. You don't design in the abstract; you build against reality. — End-to-end ownership. From business problem to architecture to a deployed, secured, monitored agent running in production and driving outcomes. You own the whole arc, not a slice of it. — The product loop. You turn what you learn in the field into reusable patterns, accelerators, and standards inside ATLAS — so the next engagement is faster and we compound into a product, not a services treadmill. — Building the team. This is the multiplier. You hire, mentor, and level up engineers to work the way you do — codifying your instinct into a playbook others can run. Success is a team who deliver like you, not you delivering everything. — Client trust. You sit across from client security, data, and business leaders and earn their confidence — in architecture reviews, security reviews, and the room where decisions get made. What we're looking for — Production track record. You've personally taken AI, ML, or agent systems into enterprise production — not demos, not pilots that died. You've worked with real client data and lived with what you shipped. — Proof you build people. You've mentored engineers, set technical standards, and shaped how a team works. Evidence you can reproduce your skill in others, not just exercise it yourself. — Range. You're equally comfortable writing code, whiteboarding architecture, and holding your own with a client's CISO. — Systems mindset. You think in reliability and trust, not cleverness — enterprise AI is won on auditability and things that keep working. — Ownership drive. You're energised by building something from early and shaping how it's done. Bonus points — Domain experience in supply chain, procurement, pricing, or other regulated / operational enterprise environments. — Exposure to enterprise security, governance, and responsible-AI practices. — A track record of growing engineers into strong, independent operators. What this role is not A back-office ML research lead. A pure people-manager who's left the tools behind. An architect who designs from a distance and never touches the client's reality. If you want to lead from a whiteboard rather than from inside the problem, this isn't it. How we work Small, high-trust, founder-close team. Governance-first, because our work runs inside clients' live operations. You'll have real authority over how we build and deliver — and the mandate to shape the team around strong talent. How to apply Tell us two stories. First: one AI system you took into enterprise production — the messy problem, how you built and secured it, and what broke along the way. Second: someone you made better — an engineer you grew, and how. Those two stories tell us more than any CV.