Machine Learning Engineer at Hiscox working on fraud detection and generative AI projects. Collaborating closely with data scientists and engineers to solve complex business challenges.
About the role
- Intermediate Machine Learning Engineer developing and implementing machine learning solutions at Sybrin. Collaborating with teams to enhance product offerings with innovative technology.
Responsibilities
- Designing and developing machine learning models and algorithms.
- Performing data preprocessing, data analysis and feature engineering.
- Evaluating model performance and tuning hyperparameters for optimization.
- Collaborating with data scientists, software engineers, and stakeholders to define project requirements.
- Implementing and deploying machine learning models into production.
- Conducting code reviews to ensure code quality and best practices.
- Debugging and resolving issues related to machine learning models and data pipelines.
- Creating and maintaining technical documentation for machine learning projects.
- Staying updated with the latest advancements in machine learning and data science.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
- Minimum of 3-5 years’ experience.
- Experience with frameworks such as PyTorch, TensorFlow, TensorFlow Light, Oynx and Scikit-learn.
- Generation of data sets, using data labelling tools such as Roboflow or CVat.
- Proficiency in using libraries such as Pandas, NumPy, and SciPy for data manipulation and analysis.
- Understanding of key machine learning and statistical algorithms including regression, classification, clustering and neural networks.
- Experience in developing, training, and evaluating machine learning models.
- Basic knowledge of deploying models using FastAPI and Docker.
- Understanding of creating RESTful APIs to integrate ML models into applications.
- Proficient in using Git and DVC for version control.
- Skills in using libraries such as Matplotlib, Seaborn, and Plotly.
- Some experience with the machine learning aspects of cloud services such as AWS, Google Cloud, or Azure.
- Experience with integrated development environments (IDEs) like PyCharm or VS Code.
- Working knowledge of the principles in ISO 9001:2015 (Quality Management System), ISO/IEC 27001:2022 (Information security, cybersecurity, and privacy management System), ISO/IEC 27701:2019 (Privacy Information Management System), POPIA, GDPR.
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Internship focusing on programming robotic arms and using machine learning in simulations at Fraunhofer IIS. Opportunity to gain practical experience and contribute to innovative research.
Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
Lead advanced machine learning model development and optimization at PayPal. Collaborate with teams to deploy scalable ML solutions in production environments.
Senior Machine Learning Engineer at Pivotal Health developing ML systems for healthcare reimbursement. Collaborating across teams to build and maintain reliable, production - grade machine learning systems.
Machine Learning Engineer working with Algorithm team on customer onboarding processes. Focus on execution and automation of models using computer vision and AI in sports industry.
Senior Machine Learning Engineer at Troveo designing and optimizing machine learning pipelines for AI video models. Collaborating with cross - functional teams to build scalable video data solutions.
AI/ML Engineer developing AI systems for data security at Nightfall. Collaborating with engineers and contributing to AI features.
Software Engineer focusing on ML infrastructure for drug discovery at Genesis AI. Leading engineering efforts to enhance scalable platforms for generative modeling and large - scale simulations.