Machine Learning Engineer

Collecting, cleaning, and pre-processing large datasets to ensure they are suitable for training machine learning models.

Designing, implementing, and fine-tuning machine learning algorithms and models to solve specific problems or achieve desired outcomes. This may involve selecting appropriate algorithms, feature engineering, and parameter tuning.

Training machine learning models using labelled data, unsupervised learning techniques, or reinforcement learning algorithms.

Assessing the performance of machine learning models using various metrics and techniques, such as cross-validation and hypothesis testing, to ensure they generalise well to unseen data.

Deploying trained models into production environments and integrating them into existing systems or workflows. This may involve building APIs, containerising models, or deploying them on cloud platforms.

Monitoring the performance of deployed models, detecting drift or degradation, and retraining models as needed to maintain performance over time.

Collaborating with cross-functional teams, including data scientists, software engineers, and other experts, to understand requirements, define objectives, and communicate findings and insights effectively.

Staying updated on the latest advancements in machine learning and AI research, experimenting with new techniques and algorithms, and contributing to the development of innovative solutions to challenging problems.

Proficiency in programming languages such as Python, R, or Java

Strong understanding of linear algebra, calculus, probability theory, and statistics is crucial for understanding and developing machine learning algorithms.

In-depth knowledge of various machine learning algorithms, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning.

Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or Keras, and understanding of neural network architectures (e.g., convolutional neural networks, recurrent neural networks).

Proficiency in data manipulation and analysis using libraries like Pandas, NumPy, and Scikit-learn, as well as experience with SQL and NoSQL databases.

Strong software engineering skills, including knowledge of software development lifecycle, version control systems (e.g., Git), debugging, testing, and deployment practices.

Ability to visualise and communicate data insights effectively using tools like Matplotlib, Seaborn, or Tableau.

Experience in feature engineering techniques to extract relevant features from raw data and improve model performance.

Understanding of evaluation metrics, cross-validation techniques, and methods for assessing model performance and generalization.

Knowledge of deployment techniques, containerisation (e.g., Docker), cloud platforms (e.g., AWS, Google Cloud Platform), and experience in deploying machine learning models into production environments.

Understanding of the specific domain or industry where machine learning solutions are being applied, including relevant terminology, challenges, and business objectives.

Strong analytical and problem-solving skills to identify, define, and address complex machine learning problems effectively.

Ability to communicate technical concepts and findings to non-technical stakeholders, collaborate effectively with cross-functional teams, and work in interdisciplinary environments.

Willingness to stay updated on the latest advancements in machine learning and AI research, attend conferences, participate in online courses, and contribute to open-source projects.