JOB SUMMARY
We’re looking for a hands-on senior engineer who has taken full architectural ownership of enterprise software, someone who designs and builds complete, standalone applications end-to-end, including the database layer, third-party integrations, and enterprise-grade security and governance controls, rather than someone who has mainly worked on top of an existing managed data or ML platform. Genuine, hands-on experience operating a live, production-scale AI system is essential, including responsibility for its reliability, monitoring, incident response, and ongoing improvement, rather than experience limited to early-stage prototyping.
This is a hands-on leadership role: part lead architect, part product owner, part governance lead. You’ll own the product’s architecture from the ground up while also bringing hands-on experience training Small Language Models (SLMs) and directing a developer focused on that work. You’ll be the technical anchor for the product — setting direction, challenging the roadmap where needed, and ensuring the system is secure, reliable, and built to scale for enterprise customers.
JOB RESPONSIBILITIES
- Take full ownership of the technical direction of the agentic AI product
- Architect and build the product end-to-end, including designing and directly owning the core database layer, rather than relying on a pre-built or managed data platform
- Build and own the integration layer connecting to third-party enterprise systems, including authentication protocols, webhooks, rate limiting, inconsistent vendor behaviour, and API versioning
- Own the event-driven architecture underpinning the platform’s core workflow and orchestration engine, including queues, workflow orchestration, and idempotency
- Implement enterprise-grade controls — role-based access control, single sign-on, audit logging, tamper-evident record-keeping, and self-hosted/on-premises deployment (Docker/Kubernetes), to meet the product’s data sovereignty requirements
- Embed application security into every layer of the architecture, from code to deployment
- Design the agentic system’s guardrails and safety constraints, manage inference-rate and latency trade-offs, and decide where to use deterministic, rule-based workflows versus AI-driven processes
- Take ownership of the operational reliability of the live system — monitoring, incident response, and ongoing improvement, not just feature delivery
- Establish and enforce governance operations — data handling, model behaviour, access controls, and change management
- Lead a small technical pod, including directing and reviewing the work of a developer focused on training the company’s Small Language Models
- Bring genuine business acumen to technical decisions — balancing customer needs, cost, and delivery timelines
- Manage requirements and delivery through Jira, maintain the codebase in GitHub, and coordinate UI build-out via Loveable
- Produce clear, thorough documentation for architecture, processes, and product decisions
- Act as the primary technical point of contact for leadership and the incoming customer base
QUALIFICATIONS
- Proven, senior-level experience designing, building, and owning production software end-to-end — this is not an entry- or mid-level role
- Track record delivering and operating complete, standalone enterprise applications, rather than building features on top of an existing managed data or ML platform
- Deep, hands-on experience designing and directly operating a relational database layer (Postgres)
- Proven experience building integrations against third-party enterprise systems — covering authentication protocols, webhooks, rate limiting, inconsistent vendor behaviour, and versioning — this is one of the most important differentiators for this role
- Strong background in event-driven systems — queues, workflow orchestration, and idempotency
- Hands-on experience implementing RBAC, SSO, audit logging, tamper-evident record-keeping, and self-hosted/on-premises deployment (Docker/Kubernetes) — essential given the product’s data sovereignty requirements
- A security-first mindset — able to proactively identify and mitigate application security risks, embedding security into every layer of the architecture
- Genuine understanding of the full stack of building an agentic application — guardrails and safety constraints, inference rates and latency trade-offs, and when to use deterministic, rule-based workflows versus AI-driven processes
- Experience with Claude Code, Postgres, and Rust (or a strong typed-language background with a demonstrated ability to ramp up quickly across this stack)
- Genuine, hands-on experience building and operating a production-grade AI or agentic system — including ownership of reliability, monitoring, incident response, and ongoing improvement, rather than experience limited to early-stage prototyping
- Hands-on experience training Small Language Models (SLMs)
- Strong working knowledge of enterprise-class systems: scalability, reliability, and security at production scale
- Track record of leading or mentoring other engineers
- Comfortable challenging specifications and proposing alternative approaches, rather than simply executing requirements as given — able to communicate and debate technical decisions effectively with internal teams and CXOs, not simply agreeing with whatever is asked
- Solid project management skills — able to plan, prioritise, and deliver against timelines with minimal oversight
- Strong documentation habits and excellent written and verbal communication
- Familiarity with GitHub, Jira, and ideally Loveable
- Product-minded: comfortable thinking about the end customer, not just the code
