backend engineer for AI workspace in finance

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.

• 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

• 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

• 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