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Open nowFirst seen 35 hours ago

Senior Machine Learning Engineer

Boardroom Appointments3,354 open roles

Where
Johannesburg, South Africa
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Your applicationOpen nowSenior Machine Learning EngineerBoardroom Appointments · Johannesburg, South Africa

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The clock on this job

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15.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 6.5%3 days
  3. 15.1%7 days
This job: first seen 35 hours ago

The posting

Senior Machine Learning Engineer - 3 Year Contract

Key Responsibilities:

  • Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency.
  • ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
  • Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.
  • Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.
  • Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.
  • MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility.
  • Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production.
  • Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting-edge research into business solutions.

Required Qualifications & Skills:

  • Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.
  • Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments.
  • Programming: Strong proficiency in Python, with experience in libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
  • Cloud & Infrastructure: Hands-on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or SageMaker.
  • Big Data & Databases: Experience with Spark, Hadoop, SQL, and NoSQL databases for handling large-scale datasets.
  • DevOps & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment.
  • Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques.
  • Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative ML solutions for complex business challenges.
  • Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively.

Preferred Qualifications:

  • Experience with NLP, Computer Vision, or Reinforcement Learning.
  • Hands-on experience with AutoML, hyperparameter tuning, and model interpretability.
  • Experience with real-time ML applications and edge AI.
  • Contributions to open-source ML frameworks or research publications.
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