Responsibilities
- Develop new models end-to-end, from understanding product requirements to implementation and deployment
- Align with various stakeholders, including Product Managers, Data Engineers, and Backend Engineers to ensure seamless integration of ML solutions into the product ecosystem
- Develop models: design, train, evaluate, and iterate on ML models using modern techniques tailored to real business problems
- Put models into production with robust technical implementation and quality assurance processes
- Create an ML Ops framework for the team to ensure our models scale effectively with proper monitoring and alerts (e.g., model drift detection, performance tracking, automated retraining pipelines)
- Share best practices within the ML team, contributing to internal knowledge, tooling improvements, and mentoring peers
Requirements
- 3+ years of experience ML Engineer coupled with ML Ops, particularly in developing client-facing products
- Experience building and optimizing machine learning models for external clients
- Proficient at writing resilient, high-quality, testable code in Python
- Understand how to integrate with third-party services and databases at scale and FastAPI or a similar web framework
- Proven track record of identifying complex problems and implementing effective solutions in machine learning contexts
- Fluent in English
Team
Team size: 11. Structure: 8 AI Engineers and 3 Data Ops
