Develop and deploy machine learning models, collaborate with cross-functional teams, and stay up-to-date with the latest developments in AI. 3-7 years of experience required. €350/day.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Machine Learning Engineer, 3 months, Fully Remote (Have to be in Europe), €350/day
Key Responsibilities:
- Model Development: Work in end-to-end analytical products, from exploration and prototyping to productizing and testing, helping to solve complex business problems across the full value chain.
- Data Pipeline Engineering: Develop and maintain data pipelines for collecting, processing, and analyzing large datasets, ensuring data quality and consistency.
- Model Deployment: Deploy machine learning models into production environments, ensuring they are scalable, reliable, and easy to maintain.
- Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, MLOps engineers, product managers, and business stakeholders, to understand business needs and translate them into machine learning solutions.
- Performance Monitoring: Continuously monitor the performance of deployed models and implement improvements to enhance their accuracy, efficiency, and scalability.
- Research & Innovation: Stay up-to-date with the latest developments in machine learning and AI, and apply new techniques to improve existing models and processes.
- Documentation: Maintain clear and comprehensive documentation of models, processes, and systems to facilitate knowledge sharing and collaboration.
Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
Experience:
- 3-7 years of experience as a Machine Learning Engineer, Data Scientist, or similar role.
- Experience in the retail industry or e-commerce is highly desirable.
Technical Skills:
- Strong problem-solving abilities, solid background in algorithms and data structures.
- Strong proficiency in Python.
- Ability to work with machine learning tools (eg. scikit-learn, tensorflow, keras, pytorch, Spark MLlib).
- Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop.
- Proficiency in SQL and experience with relational databases.
- System level architecture understanding including scaling, MLOps, model/data monitoring, and ensuring a deterministic pipeline.
- Familiarity with cloud platforms (e.g., Azure) and containerization technologies (e.g., Docker, Kubernetes).
- Experience with version control systems (e.g., Git) and collaborative development tools (e.g., JIRA, Confluence).