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.