Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to join our team on a contract basis. You will be responsible for designing, building, and deploying machine learning models into production, working closely with data scientists, software engineers, and product teams to deliver scalable AI solutions. This is an excellent opportunity for someone who thrives in fast-paced environments and enjoys solving complex problems with real-world impact.

Key Responsibilities

• Develop, train, and optimize machine learning models for production use.
• Collaborate with data scientists to turn research prototypes into production-grade solutions.
• Build robust data pipelines and feature engineering workflows.
• Deploy ML solutions into cloud environments (AWS, GCP, or Azure).
• Implement monitoring, testing, and model performance evaluation frameworks.
• Work with engineering teams to ensure seamless integration of ML models into products.
• Contribute to improving infrastructure, tooling, and best practices for ML development and deployment.

Skills & Experience

Essential:

• Strong programming skills in Python (and frameworks such as PyTorch, TensorFlow, or Scikit-learn).
• Proven experience in developing and deploying machine learning models in production.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Proficiency in working with cloud services (AWS, GCP, or Azure).
• Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes).
• Excellent problem-solving skills and ability to work independently in a fast-paced environment.

Desirable:

• Experience with NLP, computer vision, or time-series forecasting.
• Familiarity with distributed computing frameworks (Spark, Ray).
• Experience with MLOps and model governance practices.
• Previous contract experience in a similar ML engineering role.

Contract Details

• Duration: 6–12 months (extension possible)
• Location: London (Hybrid working model)
• Day Rate: Competitive, depending on experience