Senior Python Developer

Join Real Brokerage's dynamic Machine Learning (ML) team as a Senior Python Developer. You will play a crucial role in translating ML models and data pipelines into high-performance software applications, collaborating closely with ML and Data Engineers. This position involves designing and developing scalable backend systems that integrate advanced ML technologies like transformers and vector databases. As a vital member of a fast-paced team, you will ensure our ML-driven products are efficient, scalable, and maintainable. We require at least 5 years of production-level backend Python development experience, with a strong command of Python deployment and CI/CD practices.

Responsibilities:

• Collaborate with ML and Product Teams: Partner with ML engineers and product teams to seamlessly integrate Machine Learning models and data pipelines into robust backend systems, ensuring effective interaction between ML technologies and software applications.
• Backend System Design and Development: Architect, build, and maintain scalable backend systems and APIs, incorporating ML technologies such as transformers and vector databases to optimize performance and usability.
• Architect Scalable Solutions: Work with cross-functional teams to define requirements, design architecture, and implement solutions that enhance the performance, reliability, and functionality of ML-driven products.
• Code Quality and Maintenance: Lead backend component development, ensuring adherence to best practices for code quality, performance optimization, and maintainability. Refactor existing code to maintain high standards of efficiency and quality.
• CI/CD Pipeline Management: Establish and manage continuous integration and continuous deployment (CI/CD) pipelines, ensuring automated testing, smooth deployments, and proper version control throughout the development lifecycle.
• Mentorship and Technical Guidance: Provide technical leadership and mentorship to junior developers, fostering a culture of knowledge sharing and continuous learning within the team.
• Collaborate with DevOps: Work alongside DevOps teams to optimize infrastructure, ensuring smooth deployment, scaling, and monitoring of backend services.
• Troubleshooting and Performance Optimisation: Identify and resolve backend performance bottlenecks, bugs, and other challenges to guarantee system high availability and optimal performance.
• Stay Current with Technologies: Continuously explore and learn emerging technologies, tools, and best practices in backend development, machine learning, and cloud infrastructure to enhance team capabilities.

Requirements:

• Extensive backend Python development experience, with a focus on production-grade software in dynamic, fast-paced environments.
• Proficiency in integrating and working with Machine Learning technologies like transformers and vector databases within backend systems.
• Deep understanding of Python deployment best practices, including virtual environments, dependency management, containerization, and orchestrating Python applications for production.
• Proven experience designing and developing scalable backend APIs and microservice architectures.
• Familiarity with AWS services such as EC2 and S3, and the ability to architect scalable backend solutions using AWS infrastructure.
• Strong knowledge of continuous integration and continuous deployment (CI/CD) best practices and hands-on experience with automated deployment pipelines.
• Experience with data pipeline tools like Meltano, Airbyte, or similar platforms is a plus.
• Excellent problem-solving and troubleshooting skills for complex backend issues and performance bottlenecks.
• Strong communication skills for effective collaboration within cross-functional teams.
• Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
• Minimum of 6 years of professional experience as a backend Python Developer in production environments.
• Proficiency in Python, cloud services (preferably AWS), and CI/CD practices.
• Exposure to Machine Learning projects or data engineering pipelines is advantageous, though not mandatory.
• Familiarity with databases (PostgreSQL, MongoDB), cloud infrastructure (AWS, Kubernetes), and data pipeline technologies (Meltano, Airbyte) is highly beneficial.
• Experience with Kubernetes or similar container orchestration tools for managing, scaling, and deploying backend services.
• Experience working in agile development teams and contributing to iterative development cycles.