About
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.
Responsibilities
•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
Requirements
•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
Conditions
•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
