Position Overview
* We are seeking a highly skilled Machine Learning Engineer to design, build, deploy, and optimise machine learning models that power data-driven products and business solutions.
* This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance.
* The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms, and is comfortable working in a fully remote, collaborative environment within the UK.
Key Responsibilities
1. Machine Learning Model Development
* Design, develop, train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases.
* Apply supervised, unsupervised, and deep learning techniques as appropriate.
* Perform feature engineering, model tuning, and validation to improve accuracy and performance.
2. Productionisation & Deployment
* Deploy ML models into production using scalable, reliable architectures.
* Build and maintain APIs or batch pipelines for model inference.
* Monitor model performance, data drift, and retraining needs.
3. Data Engineering & Pipelines
* Collaborate with data engineers to design efficient data ingestion and transformation pipelines.
* Work with structured and unstructured data from databases, APIs, and data lakes.
* Ensure data quality, reproducibility, and versioning.
4. MLOps & Automation
* Implement MLOps practices including CI/CD for ML, model versioning, and experiment tracking.
* Use tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
* Automate model training, testing, deployment, and monitoring workflows.
5. Cloud & Infrastructure
* Build ML solutions on cloud platforms such as AWS, Azure, or GCP.
* Use containerization and orchestration tools (Docker, Kubernetes).
* Optimize compute costs and performance for training and inference workloads.
6. Collaboration & Stakeholder Engagement
* Work closely with Data Scientists, Product Managers, Software Engineers, and Analysts.
* Translate business requirements into scalable ML solutions.
* Communicate model behaviour, limitations, and results clearly to non-technical stakeholders.
7. Research & Continuous Improvement
* Stay current with advancements in machine learning, AI, and data science.
* Evaluate new algorithms, tools, and frameworks for potential adoption.
* Contribute to best practices, documentation, and knowledge sharing.
Required Skills & Experience
Core Technical Skills
* 3+ years of experience in Machine Learning, Data Science, or related roles.
* Strong programming skills in Python.
* Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost.
* Solid understanding of ML algorithms, statistics, and evaluation metrics.
* Experience deploying ML models into production environments.
Data & Engineering Skills
* Strong SQL skills and experience working with large datasets.
* Familiarity with data processing tools (Pandas, NumPy, Spark).
* Experience building APIs (FastAPI, Flask) for ML services.