Senior Machine Learning Engineer
1 week ago
3 Year Contract
Hybrid Role
Johannesburg
Job Summary:
We are seeking an experienced Senior Machine Learning Engineer to drive the development, deployment, and optimization of cutting-edge machine learning models. The ideal candidate will have a strong background in machine learning, deep learning, and software engineering, along with experience in designing scalable AI solutions.
As a Senior ML Engineer, you will work closely with data scientists, software engineers, and product teams to build robust ML pipelines, optimize model performance, and ensure seamless deployment in production environments.
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.
- Education: Bachelor's or Master's 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.
- 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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