backend engineer for AI workspace in finance
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Summary
Backend engineer at Model ML, an AI workflow platform that automates client-ready work for financial institutions. You'd build and scale Python (FastAPI) backend services and data pipelines in London, working with PostgreSQL, Redis, Celery, message brokers, Docker/Kubernetes, and security/compliance (SOC 2, GDPR, FCA), plus mentoring engineers.
Описание: Model ML builds an AI workflow platform that automates how financial institutions produce and validate client-ready work. It transforms complex manual processes into automated AI systems that scale across global teams.
Задачи:
- Design, develop, and maintain scalable backend services and APIs for the AI workspace platform
- Build and optimize pipelines that process large-scale financial datasets
- Implement security measures and ensure compliance with financial industry regulations, including SOC 2, GDPR, and FCA requirements
- Collaborate with ML engineers to productionize machine learning models and integrate them into backend systems
- Optimize database schemas and queries for high-throughput, low-latency operations across distributed systems
- Lead technical design reviews and mentor junior and mid-level engineers
- Monitor system performance, troubleshoot production issues, and improve reliability and uptime
- Contribute to engineering best practices, code quality standards, and technical documentation
- Drive technical decisions and help shape the engineering culture as the platform scales
Требования:
- At least 7 years of professional backend engineering experience building and scaling production systems
- Expert-level Python and modern web frameworks, particularly FastAPI, or similar frameworks such as Flask or Django
- Deep understanding of relational databases, especially PostgreSQL, including query optimization, indexing, and performance tuning
- Proven experience scaling databases
- Strong knowledge of caching strategies and in-memory data stores, particularly Redis
- Hands-on experience with asynchronous task processing using Celery or equivalent distributed task queues
- Proficiency with message brokers and event-driven architectures such as Azure Service Bus, RabbitMQ, or Kafka
- Solid understanding of RESTful API design and microservices architecture
- Experience with cloud platforms (Azure preferred; AWS or GCP acceptable) and containerization technologies (Docker, Kubernetes)
- Strong knowledge of security best practices, authentication and authorization mechanisms, and data encryption
- Familiarity with CI/CD pipelines, automated testing, and version control systems (Git)
- Excellent problem-solving skills and ability to debug complex distributed systems
- Strong communication skills and experience collaborating with cross-functional teams
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
Условия:
- Competitive salary and equity
- Performance-based incentives
- Opportunity to contribute to the company's market expansion
- Supportive and innovative work environment
- The role is described as very challenging; timelines and priorities may change
- Four interview stages: a 30-minute introductory call, a 30-minute technical screen, a 20-minute systems design deep dive, and an onsite interview with Engineering Leadership