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We are looking for a highly skilled Machine Learning Engineer with strong ML Ops expertise to help design, build, deploy, and manage machine learning pipelines in production environments. The ideal candidate will have a strong foundation in machine learning algorithms, deep learning, data engineering, and experience in automating the deployment, monitoring, and scaling of models through ML Ops practices.
Responsibilities:
- Model Development: Design and develop machine learning models to solve complex problems using a variety of algorithms, including supervised, unsupervised, and reinforcement learning techniques.
- ML Ops Implementation: Develop and deploy ML workflows, integrating version control, automation, and monitoring practices to streamline model development and deployment.
- Pipeline Development: Build and maintain end-to-end machine learning pipelines for continuous integration, continuous deployment (CI/CD), and model retraining.
- Automation: Automate model training, testing, and deployment processes, ensuring high levels of efficiency and reliability in production.
- Model Optimisation: Collaborate with data scientists and engineers to optimise models for production environments, focusing on scalability, speed, and resource efficiency.
- Cloud Integration: Utilise cloud platforms (e.g., AWS, Azure, GCP) for model deployment and infrastructure management, ensuring smooth scaling and resource optimisation.
- Documentation: Maintain clear and comprehensive documentation for model workflows, ML Ops processes, and system architectures.
