Machine Learning Engineer

Experience 4+ years of relevant experience in Python development and Machine Learning. Job Summary We are seeking an experienced Machine Learning Engineer with strong Python programming skills and practical experience developing, implementing, and optimising machine learning solutions. The ideal candidate will have domain knowledge in text and audio processing, hands-on experience with machine learning frameworks, and an understanding of data engineering, feature stores, ETL pipelines, and deployment environments. The successful candidate will be responsible for developing scalable ML solutions, exploring emerging technologies, and collaborating with cross-functional teams to deliver reliable, data-driven applications. Key Responsibilities - Design, develop, train, test, and optimise machine learning models using Python. - Work on machine learning solutions involving text and audio data, applying relevant domain knowledge and feature engineering techniques. - Use appropriate ML frameworks and libraries to build, evaluate, and improve model performance. - Develop and maintain data processing pipelines, ETL workflows, and feature engineering processes. - Work with data warehouses, feature stores, and non-relational databases to support ML applications. - Integrate machine learning models with web frameworks and application environments. - Manage development environments using virtual environments and Docker containers. - Monitor, evaluate, and document model performance, experiments, and results. - Collaborate with data engineers, software developers, and other stakeholders to deliver production-ready solutions. - Stay up to date with emerging machine learning techniques, tools, frameworks, and industry best practices. Essential Skills and Experience - Python programming: At least 4 years of proven coding experience in Python, with practical Machine Learning experience. - Machine Learning: Strong understanding of ML concepts, model development, training, evaluation, and optimisation. - Text and audio processing: Practical experience working with text and audio data, including relevant domain features and processing techniques. - ML frameworks and libraries: Hands-on experience with relevant tools and frameworks, including: - Scikit-learn - Keras - PyTorch - TensorFlow - TensorBoard - MLflow - spaCy and NLTK - Pandas and NumPy - Jupyter Notebook - Seaborn - Data engineering: Experience working with data warehouses, feature stores, ETL pipelines, and data preparation workflows. - Databases: Experience with non-relational (NoSQL) databases. - Web frameworks: Experience integrating or deploying ML solutions using web frameworks. - Development environments: Experience using Python virtual environments and Docker containers. - Problem-solving: Strong analytical and troubleshooting skills, with the ability to investigate complex technical challenges. - Adaptability: Ability to quickly learn new technologies and apply them effectively. - Continuous learning: Demonstrable interest in keeping up to date with advances in the machine learning community. Desirable Skills - Experience deploying and maintaining machine learning models in production environments. - Knowledge of Natural Language Processing (NLP), speech processing, and audio feature extraction. - Experience with model tracking, experiment management, and performance monitoring. - Familiarity with scalable ML pipelines and containerised application deployment. - Experience working in Agile development teams. Personal Attributes - Strong communication and collaboration skills. - A proactive approach to learning and technical problem-solving. - Ability to work independently and as part of a multidisciplinary team. - Attention to detail and commitment to producing high-quality, maintainable code. What We Are Looking For A hands-on Machine Learning Engineer who combines strong Python development experience with practical ML expertise, particularly in text and audio domains. The ideal candidate will be adaptable, technically curious, and capable of contributing to the development of robust, scalable machine learning solutions.