WHO WE ARE:

Profylr is a SaaS, product-led RegTech company building the future of regulatory technology.

We combine AI, data engineering and modern cloud architecture to create intelligent platforms that transform how financial institutions build, evidence and scale regulatory capability. Our technology helps organisations move beyond manual processes, disconnected systems and static compliance tools.

We are building a multi-product suite designed to operate across regulatory domains and become a core intelligence layer within financial institutions.

We exist to create the next generation of regulatory technology.

WHY US:

Profylr is a place for people who want to build modern technology with real-world impact.

Our engineers work across the full product — from data and AI services through to backend systems and user-facing experiences — with real exposure to architecture, product direction, and the challenges facing financial services.

Here, you’ll do more than just work. You’ll innovate. You’ll contribute directly to our cutting-edge Information Genetics® technology and work on solutions that tackle real industry problems. You’ll be part of a highly collaborative team working with emerging technologies, shaping a growing suite of solutions, and building capabilities that have never existed before in regulatory technology.

We actively invest in creating an environment where people can grow, explore ideas, and demonstrate their natural ability. Problem solvers, creative thinkers and driven professionals thrive here — supported to develop their skills while helping shape the future of regulatory technology.

THE ROLE:

You’ll help build and scale cloud-based SaaS solutions that uses AI to power intelligent regulatory capability across financial services.

This is a hands-on, full-stack engineering role. You’ll own features end-to-end — from data modelling and APIs through to React user experiences. You’ll work on systems that ingest complex regulatory and operational data and turn it into explainable reporting, analytics and evidence.

This role suits someone who enjoys working on data-intensive platforms, cares deeply about engineering quality, and wants real ownership over what they build.

RESPONSIBILITIES:

·      Design and build features across React (TypeScript) and Python (FastAPI)

·      Design schemas, optimise queries and manage PostgreSQL migrations

·      Build secure, scalable REST APIs for a multi-tenant SaaS platform

·      Develop services that support AI-driven reporting and analytics

·      Integrate AI capabilities for regulatory interpretation, evidence mapping and insight generation

·      Write comprehensive automated tests

·      Work within structured engineering practices (feature branches, PRs, CI/CD)

·      Contribute to architectural decisions as the platform scales

TECH STACK

·      Frontend: React, TypeScript, Tailwind

·      Backend: Python, FastAPI

·      Data & Search: PostgreSQL, Typesense

·      AI & Analytics: LLM and data-driven services integrated into the platform

·      Infrastructure: Docker, GitHub Actions, AWS

·      Quality: Static typing, linting, automated unit, integration and E2E testing

WHAT YOU NEED TO UNDERSTAND

Frontend Development

• React - Component architecture, hooks, state management patterns
• TypeScript - Type safety as a development practice, not just syntax
• Modern UI patterns - Component libraries, utility-first CSS, responsive design
• Testing - Unit tests, component tests, E2E testing strategy

Backend Development

• Python - Async programming, type hints, clean code practices
• API design - RESTful principles, error handling, documentation
• Authentication - OAuth2/JWT flows, role-based access control
• Framework patterns - Dependency injection, middleware, service layers

Data Layer (Essential)

The platform handles complex data relationships and high-volume operations - strong database skills are critical.

• PostgreSQL - Query writing, indexing, performance tuning
• ORM usage - When to use it, when to write raw SQL, avoiding common pitfalls
• Optimisation techniques - Query diagnostics, efficient data patterns
• Search engines - Full-text search concepts

Engineering Practices

• Version control - Branching strategies, meaningful commits, PR workflow
• CI/CD - Automated testing, deployment pipelines, quality gates
• Containerisation - Docker for development and deployment
• Test-driven development - Writing tests first, tests as documentation
• Security mindset - OWASP awareness, input validation, secure defaults

SaaS & Cloud Thinking

Essential understanding for this role:

• Multi-tenancy - How to isolate data, scope queries, share infrastructure safely
• API-first design - Building for multiple consumers, versioning, rate limiting
• Cloud patterns - Stateless services, configuration management, secrets handling
• Scalability - Connection pooling, caching strategies, async processing
• Observability - Logging that helps debugging, health checks, error tracking

AWS experience is valuable but not essential if you understand cloud concepts.