Senior Machine Learning Engineer
4 weeks ago
Job Description
Qualifications:
- Master’s or PhD in Computer Science, Machine Learning, Mathematics, Statistics, or related fields.
- Minimum of 5+ years of hands-on experience in machine learning, including expertise in deep learning, NLP, or computer vision technologies.
- Proficient in programming languages relevant to data science and machine learning, especially Python, and skilled in using frameworks such as TensorFlow or PyTorch.
- Strong foundational understanding of machine learning algorithms, data structures, and software engineering principles.
- Demonstrated experience with end-to-end machine learning project cycles, from conceptualisation to deployment.
- Familiarity with cloud computing platforms (AWS, Google Cloud, Azure) for deploying scalable ML solutions.
- Practical experience with data processing, data pipeline frameworks, and ETL processes.
- Ability to work collaboratively in a fast-paced startup environment, with strong problem-solving skills and adaptability.
- Effective communication skills for translating complex technical details into actionable business insights.
Responsibilities:
- Lead the development and optimisation of machine learning models to enhance product features and drive innovation.
- Collaborate with cross-functional teams, including product development and data engineering, to integrate AI solutions into scalable products.
- Design and execute experiments and analyse data to refine models, using both supervised and unsupervised learning techniques.
- Implement best practices for data modelling, validation, and architecture to ensure high-quality and efficient production systems.
- Engage in knowledge sharing by mentoring team members, contributing to internal workshops, and staying updated with industry trends.
- Utilise cloud-based architectures for deploying machine learning models at scale, ensuring performance and reliability.
- Continuously evaluate the performance of models in production, applying MLOps principles for model monitoring, retraining, and versioning.
- Identify and adopt new technologies and methodologies to solve business challenges and improve the ML development lifecycle.
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