Python Backend Engineer

Summary

Join Tide's ML Engineering team as a Python Backend Engineer (Data & AI) and contribute to building a finance platform for small businesses. Work closely with product owners and other engineers to translate requirements into well-engineered solutions. You will architect, design, test, implement, deploy, monitor, and maintain backend services, integrating pre-trained ML models. Collaborate with cross-functional teams to ensure efficient project execution and align with the target architecture. This role requires proven backend Python engineering experience, understanding of software engineering fundamentals, and experience with various frameworks and technologies. Tide offers a flexible workplace model supporting both in-person and remote work, along with benefits such as paid annual leave and sabbatical leave.

Requirements

• Proven work experience as a Backend Python Engineer (Senior/Lead)
• Understanding of software engineering fundamentals (OOP, SOLID, etc.)
• Hands-on experience with frameworks such as Django, FastAPI or Flask
• Extensive experience integrating with or creating REST APIs
• Experience with creating and maintaining CI/CD pipelines - we use GitHub Actions
• Experience with event-driven architectures
• Experience with AWS

Responsibilities

• Work closely with product owners, other backend (Java & Python) engineers, data scientist and other business members to translate requirements into well-engineered solutions
• Architect, design, test, implement, deploy, monitor and maintain end-to-end backend services. You build it, you own it mentality
• Integrate already trained ML models in some of the services you develop
• Work with people from other teams and departments on a day to day basis to ensure efficient project execution with a focus on delivering value to our members
• Regularly aligning your team’s vision and roadmap with the target architecture within your domain and to ensure the success of complex multi domain initiatives

Preferred Qualifications

Experience with Databricks, ML feature store solutions, Kafka (or other message brokers)

Benefits

• 25 days paid annual leave
• Sabbatical Leave
• Share options
• Flexible workplace model that supports both in-person and remote work