Company Profile:

Job description:

Job Description – Machine Learning Engineer

Candidate needs to be an expert in machine learning to extract value from data and Will lead all of the processes from data collection, cleaning, and pre-processing, to training models and deploying them to production.

Responsibilities

• Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability.
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
• MLops lifecycle management , develop, test, deploy and monitor
• Experience in using Business Intelligence tools such as PowerBI, Kibana etc.
• Verifying data quality, and/or ensuring it via data cleaning.
• Supervising the data acquisition process if more data is needed.
• Finding available datasets online that could be used for training.
• Defining validation strategies.
• Defining the pre-processing or feature engineering to be done on a given dataset.
• Defining data augmentation pipelines.
• Training models and tuning their hyperparameters.
• Analysing the errors of the model and designing strategies to overcome them.
• Deploying models to production.
• Ability to utilise cloud resources and functionalities to build and deploy ML models.
• Ability to containerise and productionise ML models and creating end points for inference.

Required Skillset:

• Bachelor's degree in computer science, data science, mathematics, or a related field.
• Master's degree in computational linguistics, data analytics, or similar will be advantageous.
• At least two years' experience as a machine learning engineer.
• Advanced proficiency with Python, Java, and R code writing.
• Extensive knowledge of ML frameworks, libraries, data structures, data modelling, and software architecture.
• In-depth knowledge of mathematics, statistics, and algorithms.
• Machine Learning, Deep Learning & Exploratory Data Analysis
• TensorFlow, Keras, Python, Pandas
• Natural Language Processing, Text analytics / Text mining and Computer vision
• Pattern Recognition
• Containerization of ML models