Head of Engineering

About the Role

We're looking for a Head of Engineering to lead the design, build, and operation of our cloud-native platform and the AI-native products that run on top of it. This is a hands-on leadership role: you will oversee technical direction, grow the team, and stay close enough to the architecture to make and defend hard calls.

You will partner closely with Product, Security, and customer-facing teams. The role suits someone who has spent a significant number of years operating distributed systems at scale and has more recently gone deep on building production LLM applications and agents — not just prototyping them.

Responsibilities

Engineering leadership. Lead, hire, and grow a multi-disciplinary engineering team spanning platform, application, and AI/ML. Set the technical bar, define hiring rubrics, run performance and growth conversations, and shape the engineering culture.

Building and leading cross-functional teams. Stand up and lead cross-functional pods that bring together engineering, product, design, security, and AI/ML specialists around clear outcomes. Establish operating rhythms, decision-making norms, and feedback loops that let mixed-discipline teams move quickly and ship cohesively.

Architecture and technical strategy. Oversee the end-to-end architecture for our cloud-native platform and AI products. Make build-vs-buy calls, evaluate vendors, and decide where we invest in differentiation versus where we adopt off-the-shelf components.

Platform and infrastructure. Drive the evolution of our Kubernetes-based platform, including networking, service mesh, observability, CI/CD, secrets management, and developer tooling. Push the team toward strong golden paths, sensible defaults, and operational maturity (SLOs, on-call hygiene, incident response, postmortems).

AI-native product engineering. Lead the design and delivery of LLM-powered applications and agentic systems — including agent orchestration, tool use, evaluation harnesses, retrieval pipelines, sandboxing, and inference infrastructure (self-hosted and provider-based). Set the standards for how we evaluate, monitor, and safely deploy non-deterministic systems.

Reliability, security, and cost. Own production reliability targets and the security posture of everything we ship. Drive cost discipline across compute, inference, and storage.

Cross-functional execution. Translate product strategy into technical roadmaps and quarterly plans. Communicate trade-offs clearly to non-technical stakeholders, including customers and executives.

Qualifications

Required

• Minimum 15+ years of software engineering experience, with at least 5–7 years in senior engineering leadership (managing managers, or leading teams of 20+).
• Proven ability to build and lead cross-functional teams — hiring across disciplines, setting shared goals, resolving conflict, and creating an environment where engineers, product managers, designers, and AI/ML specialists do their best work together.
• Deep, hands-on experience designing and operating cloud-native applications in production — microservices, event-driven systems, async workflows, and the operational realities that come with them.
• Strong Kubernetes background: not just deploying to it, but understanding its internals, networking, multi-tenancy, operators, and the ecosystem around it (Helm, Argo, service mesh, ingress, policy engines).
• Substantial infrastructure and platform engineering experience: building internal platforms that other engineers actually want to use, with a clear point of view on developer experience, golden paths, and platform-as-product.
• Demonstrated experience building AI-native applications and agents in production — meaning real LLM-powered systems with proper evaluation, tracing, retrieval, tool use, and guardrails, not just demos. Familiarity with agent frameworks (LangGraph, OpenAI Agents SDK, or equivalent), inference proxies, and evaluation tooling (RAGAS, Inspect, Braintrust, or similar).
• Track record of hiring senior engineers, building healthy engineering cultures, and shipping reliably under real constraints.
• Strong written and verbal communication. You can explain a hard technical trade-off to a customer, a board member, and a junior engineer — and be understood by all three.

Good to have

• Experience deploying and operating systems in air-gapped or restricted environments (regulated industries, sovereign cloud, defence, on-prem enterprise) — including the realities of offline package management, image distribution, model deployment without internet access, and compliance overhead.
• Experience with workflow orchestration systems (Temporal, Argo Workflows) and streaming platforms (Kafka, Flink).
• Experience with API gateways and identity (Kong, Envoy, OIDC, mTLS).
• Familiarity with self-hosted inference (vLLM, SGLang) and open-weight model deployment.
• Background in security, particularly around AI agent sandboxing and least-privilege execution.
• Prior experience as a founding or early-stage engineering leader.

How We Work

We value strong written communication, small focused teams, and engineers who care about the operational consequences of what they build. We expect leaders to stay close to the code and the customers, and to make decisions that hold up under scrutiny months later — not just at the planning meeting.

What You will Get

• Significant ownership over engineering execution and team outcomes.
• A flat, decision-oriented environment with direct access to founders and customers.
• Competitive compensation including performance-based equity option compensation.
• Budget for hardware, learning, and the tools you need to do good work.

If you've spent your career making distributed systems boring and you're now spending your weekends figuring out how to make agents reliable, we should talk.