Intermediate Machine Learning Engineer; Hybrid

Intermediate Machine Learning Engineer (Hybrid)

Sybrin Johannesburg, Gauteng, South Africa

About Sybrin:
Sybrin is a leading IT software development company specialising in innovative solutions tailored to meet the evolving needs of businesses across various sectors. Our mission is to empower businesses with cutting-edge technology solutions that drive efficiency, enhance customer experiences, and facilitate growth.

Role Overview:
This role will be responsible for developing and implementing machine learning solutions that integrate seamlessly with the overarching product space being developed within the company. This involves designing, building, and deploying machine learning models and algorithms that address complex business problems and enhance the company's product offerings.

• Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.
• Minimum 3-5 years of experience.

Machine Learning Team Lead

• 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.

• Machine Learning Frameworks:
  Experience with frameworks such as PyTorch, Tensor Flow, Tensor Flow Lite, ONNX and scikit-learn.
• Data Labelling:
  Generation of datasets using data labelling tools such as Roboflow or CVat.
• Data Processing:
  Proficiency in using libraries such as Pandas, Num Py, and Sci Py.
• Algorithms:
  Understanding of key machine learning and statistical algorithms including regression, classification, clustering and neural networks.
• Model Development:
  Experience in developing, training, and evaluating machine learning models.
• Model Deployment:
  Basic knowledge of deploying models using FastAPI and Docker.
• APIs:
  Understanding of creating RESTful APIs to integrate ML models into applications.
• Version Control:
  Proficient in using Git and DVC.
• Data Visualization:
  Skills in using Matplotlib, Seaborn, and Plotly.
• Cloud Platforms:
  Some experience with cloud services such as AWS, GCP, or Azure.
• IDE:
  Experience with IDEs like PyCharm or VS Code.
• Analytical thinking, problem solving, effective communication.
• ISO and privacy knowledge (ISO 9001, ISO/IEC 27001, ISO/IEC 27701, POPIA, GDPR).

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