Machine Learning Engineer

We are seeking a highly motivated and experienced Machine Learning Engineer to join our team. You will be responsible for transforming Data Science prototypes into production-ready applications, building scalable MLOps pipelines, and maintaining deployed models. This role requires a strong background in software engineering, cloud infrastructure, and practical experience with the full ML lifecycle.

Key Responsibilities:

• Production Deployment & Scaling:

• Refactor and optimise Data Science prototypes for performance and efficiency.
• Containerise models using Docker and deploy them onto a scalable infrastructure, such as Kubernetes.
• Design and build low-latency APIs to serve model predictions in real-time.

• MLOps Pipeline Automation:

• Implement CI/CD/CT pipelines to automate model training, testing, and deployment processes.
• Utilise orchestration tools like Airflow or Kubeflow to manage and schedule complex ML workflows.

• Advanced AI Focus:

• Work with specialised models (e.g., LLMs) on tasks such as fine-tuning and prompt engineering.
• Contribute to ensuring the ethical and compliant use of AI systems, particularly in relation to data privacy and bias.

• System Monitoring & Maintenance:

• Set up robust monitoring for model performance, data drift, and infrastructure health in production.
• Develop and manage automated processes for model retraining, versioning, and rollbacks.

• Software Engineering & Collaboration:

• Write high-quality, clean, modular Python code following professional software engineering standards.
• Collaborate closely with Data Scientists and Data Engineers to ensure seamless data flow and feature availability for models.