Director of Product & Engineering

I recently left McKinsey to build again.

After years working on business building and GenAI in regulated mid-market environments, one thing became clear:

The real bottleneck in AI is not capability. It is turning that capability into reliable systems that operate inside real businesses.

Most companies experiment with AI. Very few build systems that are governed, production-grade, and embedded in operational workflows.

We are building an AI-native company focused on closing that gap.

Not with pilots. Not with slide decks. But by deploying governed AI systems into real operational processes - and creating measurable value in weeks.

Finance. Back office. Operational workflows.

To build the underlying platform and product that make this possible, I am looking for a.

We do not build the platform in isolation. We build it inside real, paying client engagements in regulated industries - currently banking and healthcare - and we own the reusable IP that emerges. The first engagements are the birthplaces of the first platform components.

This is a deliberate choice. It means the platform runs in production from day one, in regulated environments, with real operational data and real consequences. It also means it is funded by client revenue from day one.

The role requires someone who sees this clearly: that in this phase, the engagement is the engineering environment. That building inside real operations is not a constraint on platform thinking - it is the only path to a platform that survives contact with reality.

This is a foundational product and engineering leadership role. A co-development partner for the founder.

You will help define:

• what we build
• how we build it
• and how product and engineering should evolve as the company scales

This role sits at the intersection of product thinking, system architecture, and real-world operational deployment.

You will not inherit a traditional engineering organization. You will help build a small, elite product and engineering team operating in an AI-native way, with coding agents deeply embedded into how work gets done.

This is a builder role first.

• Design the architecture of an AI-native operations platform
• Translate real operational pain points - observed inside live engagements - into scalable product components
• Define clear boundaries between deterministic workflows and agentic systems
• Ensure reliability, observability, and governance in production
• Build a small, elite product and engineering organization
• Integrate coding agents and automation deeply into the development workflow
• Establish an AI-native way of building product, where human judgment and machine leverage are combined by design
• Create fast development loops without compromising core system guarantees
• Work hands-on on core platform components in the early phase
• Operate as a senior technical lead inside client engagements, where the platform is being built and proven
• Turn messy operational workflows into stable systems
• Close the gap between product design, architecture, and production reality

• Designing safe execution boundaries between deterministic and agentic logic
• Building workflow infrastructure for long-running, stateful operational processes
• Managing integrations across ERP, finance, inbox, and document systems
• Making AI systems observable, governable, and debuggable in production
• Handling retries, partial execution, failure modes, and approval logic across complex workflows
• Designing systems that move fast while preserving trust, control, and auditability
• Building an engineering environment where coding agents are part of the default development model

• Strong backend engineering background, ideally in Python, Type Script, Node.js, or similar environments
• Experience designing distributed systems, APIs, and workflow-based architectures
• Strong product instinct and the ability to turn ambiguous operational problems into clear product decisions
• Comfort operating in client-facing contexts where the platform is being built inside real engagements
• Comfort with LLM-based systems, tool-based agent architectures, and workflow…