Senior Backend Engineer

• 5+ years of software engineering experience.
• Strong backend fundamentals with experience in languages such as Python, C#, JavaScript, Go, or similar.
• Comfortable operating in ambiguous, fast-moving startup environments.
• Experience building scalable systems, APIs, and cloud-native applications.
• Exposure to data platforms, data architecture, or complex data ecosystems is a plus.
• Experience with SDK development, public APIs, or security-focused engineering is beneficial.
• Naturally curious, hands-on, and willing to work outside traditional role boundaries.

If you’re excited about helping enterprises understand, govern, and maximise the value of AI at global scale, apply now!

Harrison Clarke are partnered with a venture-backed AI infrastructure company building the financial operating system for enterprise AI. As AI adoption accelerates, enterprises are spending millions on models, inference, tooling, and AI workflows, often with limited visibility into cost, governance, or business impact. This company is building the platform that helps organisations understand where AI spend is happening, measure ROI, and make better decisions about how AI is deployed across the business.

Engineers move across problems, take ownership beyond their immediate area, and work closely with product and leadership teams to shape what gets built. One month you might be designing backend services and APIs; the next you could be working with data pipelines, infrastructure, integrations, or customer-facing product features.

The team values curiosity, speed, and people who naturally gravitate toward solving problems rather than escalating them.

Role Requirements

• Design, build, and operate backend services powering enterprise AI products.
• Work across product, data, and infrastructure to deliver features end-to-end.
• Help shape the next generation of a platform moving from technical users to executive stakeholders.
• Build integrations, APIs, SDKs, and systems that connect data from a wide variety of enterprise sources.
• Work with large-scale datasets, cloud infrastructure, and distributed systems.
• Leverage AI tools as a natural part of the engineering workflow.