Senior Software Engineer

We build data-heavy FInancial software: analytics, pricing and modelling tools that process large datasets and turn them into decisions. You'll own features end to end, from schema and APIs to UI and deployment, on a small team that ships to production every week. Build and maintain backend services and APIs in Python (FastAPI and Django). ● Design PostgreSQL schemas, write efficient queries, and handle migrations. ● Build product UIs in React (Next.js is a plus). ● Process large datasets (hundreds of thousands of rows) reliably within memory and time limits. ● Turn actuarial and statistical logic into reliable, testable code. ● Deploy, monitor and debug services on cloud VMs, and keep production stable.

4–5 years of professional Python, with hands-on Django and FastAPI. 2. 2–3 years with PostgreSQL, including designing backend schemas: normalisation, indexing, migrations. 3. Production experience with React.js or Next.js. 4. A good understanding of system-design fundamentals: API design, caching, queues, concurrency, scaling and failure handling. 5. Working knowledge of Azure or AWS: VMs, serverless (Azure Functions / AWS Lambda), storage and networking basics. 6. Comfortable on Linux in production: Docker, nginx, systemd, logs. You can debug a slow or crashing service from the server itself. You break a vague or complex problem into clear, testable steps. ● You find root causes rather than patching symptoms, using logs, profiling and data. ● You can work through unfamiliar domain logic, such as actuarial formulas or statistical models, and get it right. ● You weigh trade-offs (speed, accuracy, cost, simplicity) and explain your choices. pandas and NumPy for large-scale data processing; profiling memory and CPU. ● Background jobs with Celery and Redis, or similar task queues. ● MongoDB alongside Postgres. ● Integrating LLM APIs (Azure OpenAI / OpenAI) for structured data extraction. ● CI/CD and deployment scripting (bash, GitHub Actions). ● Exposure to actuarial, statistical or financial modelling. You own what you ship, including how it behaves in production. ● You write clear, simple code and can explain trade-offs. ● You're comfortable with ambiguous problems and messy real-world data. ● You can understand actuarial stakeholders’ needs and translate them into clear system requirements, workflows, and reliable software.