Machine Learning Engineer

Summary

• Design, implement, and deploy machine learning algorithms to solve complex problems.
• Collaborate with data scientists to transform data models into scalable production applications.
• Optimise algorithms for performance and scalability.
• Evaluate and adapt to newer and emerging ML technologies and methodologies.
• Stay updated with the latest research and techniques in machine learning and artificial intelligence.

Key Skills

1. Proficiency in machine learning frameworks like TensorFlow, PyTorch, and Keras.
2. Strong programming skills in Python, Java, or similar languages.
3. Experience with big data technologies such as Spark, Hadoop, and Kafka.
4. Knowledge of deep learning, neural networks, and NLP.
5. Ability to work with large datasets and knowledge of data preprocessing techniques.

Standard Industry Training

• TensorFlow Developer Certificate
• Deep Learning Specialisation (Coursera)
• AWS Certified Machine Learning – Specialty

Interview Questions:

1. Describe a challenging problem you've tackled using machine learning and the approach you took.
2. How do you handle imbalanced datasets?
3. Explain the difference between L1 and L2 regularisation.
4. How do you decide which machine learning algorithm to use for a particular problem?
5. Describe a situation where a model you developed didn't perform as expected and how you addressed it.