We are hiring an experienced Machine Learning Engineer to join our team. This specialist will be responsible for refactoring our ML codebase, integrating new infrastructure, and developing reusable modules. Working closely with our data science and engineering teams, the Machine Learning Engineer will play a key role in enhancing the maintainability, scalability, and performance of our ML systems.
About project:
Join our client’s innovative team, working at the forefront of agricultural technology. They are dedicated to developing cutting-edge solutions that combine IoT and AI to optimize processes in smart agriculture. The core objective of this project is to monitor and improve crucial natural processes, thereby enhancing efficiency and sustainability in the agricultural sector.
Our client’s ML infrastructure processes data from IoT sensors placed in beehives to monitor temperature, humidity, and other environmental factors. This data is used to create predictive models focused on colony health, queen problems, and pollination efficiency. By leveraging real-time data and advanced technologies, the platform helps to drive better outcomes for food production and biodiversity, making a tangible impact on global food systems. The team is looking for someone who can help improve the underlying architecture of these systems while maintaining their functionality and accuracy.
Your area of responsibility: * Restructuring and optimizing existing Machine Learning code to enhance its readability, ease of maintenance, and performance, all while ensuring core functionality is preserved. * Integrating ML models and their associated pipelines with recently developed infrastructure elements to guarantee smooth operation and compatibility. * Designing and implementing reusable Machine Learning modules intended for common tasks such as preparing data, engineering features, and training or evaluating models. * Developing and deploying both unit and integration tests to uphold code quality standards and confirm reliability. * Identifying and putting into practice improvements aimed at boosting model performance, increasing efficiency, and supporting scalability. * Collaborating effectively with colleagues who are not located in the same place, offering guidance and participating in code review processes to receive feedback.
Skills and requirements: * A minimum of 5 years’ professional experience in software engineering, with a strong focus on Machine Learning systems. * Demonstrated high proficiency in Python and essential ML libraries (such as TensorFlow, PyTorch, and scikit-learn). * A proven history of successfully refactoring complex existing codebases without compromising original functionality. * Experience in the design and deployment of software components and libraries intended for reuse. * Familiarity with standard ML pipelines, including processes for handling and deploying data. * Knowledge of fundamental software engineering practices, such as managing versions, testing code, and creating documentation. * Experience working with systems that are distributed and software stacks that are cloud-based. * Strong capabilities in solving problems, with a focus on creating solutions that are practical and easy to maintain over time.
Will be a plus: * Practical experience with MLOps tools and common practices in that field. * Understanding and experience with technologies used for containerization and orchestration (like Docker or Airflow). * Familiarity with continuous integration/continuous deployment (CI/CD) pipelines specifically for ML systems. * Experience using tools designed for data versioning. * Exposure to frameworks used for processing data on a large scale. * Experience working effectively with teams that are geographically dispersed and in roles such as consulting or contract work.
We offer: * Working in a team of talented and passionate engineers; * Opportunity to work with the most trending technologies; * Long-term enjoyable cooperation; * Personal legal support; * English classes; * Paid vacation and sick days; * Competitive salary depending on your own talents; * Regular performance & career development reviews; * Team building events.
