Machine Learning Engineer
- Advanced skill in Computer Science/Math and Statistics skills (linear algebra, calculus, Bayesian statistics, mean, median, variance, etc.) or a related discipline.
- Robust data modelling and data architecture skills.
- Programming experience in Python, R, Java, C++, etc.
- Knowledge of Big Data frameworks like Hadoop, Spark, Pig, Hive, Flume, etc.
- Experience in working with ML frameworks like TensorFlow and Keras.
- Worked with ML libraries and packages like Scikit learn, Theano, Tensorflow, Matplotlib, Caffe, etc.
Responsibilities of a Machine Learning Engineer:
- To study and convert data science prototypes.
- To design and develop Machine Learning systems and schemes.
- To perform statistical analysis and fine-tune models using test results.
- To find available datasets online for training purposes.
- To train and re-train ML systems and models as and when necessary
- To extend and enrich existing ML frameworks and libraries.
- To develop Machine Learning apps according to customer/client requirements.
- To research, experiment with, and implement suitable ML algorithms and tools.
- To analyze the problem-solving capabilities and use-cases of ML algorithms and rank them by their success probability.
- To explore and visualize data for better understanding and identify differences in data distribution that could impact model performance when deploying it in real-world scenarios.
Machine Learning Engineers