Senior Software Engineer

This is a backend-heavy position with a strong focus on platform and infrastructure. You’ll be designing and building the scaffolding that supports agentic workflows, data movement, and system reliability at scale.

• Own and build core backend systems in Python, with a focus on structure, typing, and maintainability
• Design platform components that support orchestration, workflows, and system reliability
• Define data models, schemas, and internal abstractions that scale with product complexity
• Build APIs and internal tooling that enable fast, safe iteration across the product
• Contribute to architectural decisions that shape how the entire system evolves
• Work closely with product and end users to ensure systems solve real problems, not theoretical ones, * Developing backend services using Python, likely with frameworks such as FastAPI or Django
• Designing systems that handle real-world edge cases, failures, and scale constraints
• Building infrastructure that supports evaluation, observability, and control of AI-driven workflows
• Integrating external services and data sources into a cohesive, reliable platform
• Improving performance, resilience, and clarity across the backend stack
• Taking ownership of problems from early idea through to production

• Strong backend engineering experience, ideally in Python
• Experience building platform or infrastructure systems, not just application layers
• Solid understanding of system design, distributed systems, and scalable architecture
• Experience working in fast-paced or early-stage environments
• Comfort with ambiguity and a tendency to take ownership rather than wait for direction
• Product awareness, you understand how backend decisions impact real users

This is not a role for someone who wants neatly defined tickets and clear boundaries. You’ll be working in the messy middle, where systems, product, and real-world constraints collide. For the right person, it’s the kind of role that sharpens how you think about software, not only how you write it.

If you’re more interested in the systems that make AI work than the models themselves, this is where things get interesting.

We’re working with a high-calibre startup building an agentic AI product, but the real challenge sits underneath that. This is about the infrastructure, tooling, and platform layer that allows AI systems to behave reliably in production, not just look good in a demo.

This is an opportunity to build foundational systems properly, rather than inheriting a mess six months too late. Backed by serious investors and led by people who’ve built before, the environment is demanding but grounded in reality.