Machine Learning Engineer at Indicina

About the role

The Machine Learning Engineer role is a crucial part of our Data Science team. This is a great role for you if you are a current Software Engineer and want to work cross-functionally, yet specifically with Machine Learning algorithms, or if you are a current Data Scientist who has knowledge of how the algorithms work, but want to focus more on the Software Engineering, Data Engineering, and deployment of models. You will be handed Data Science models that have been developed by our ML team and you will work on optimizing the Data Science code and deploying the models to production. You can expect to work with Data Scientists to connect the gap from testing to production within our company software with the practice of both Data Engineering and DevOps tools.

Job Responsibilities:

• Design, prototype, and refine scalable infrastructure for operating Indicina’s machine learning pipeline at scale
• Automate the infrastructure maintenance and management for tracking all our ML deployments
• Understand the business problem and the Data Science solution built by the ML team and study and understand the general concepts of the Machine Learning algorithm(s) used
• Understand how often the models will need to be trained, tested, and deployed, and how many predictions will be made and on what frequency basis.
• Use your expertise to automate the data science code and the whole workflow from input to prediction implementing the model within the app or software of the company
• Ultimately optimize the model itself with Data Engineering techniques like data storage and OOP improvements
• Work on versioning (like Git/GitHub) and monitoring of training/predictions
• Be the ultimate responsible for the code repository creation and efficiency
• Research and implement MLOps tools, frameworks and platforms for our Data Science pipeline.
• Work on a backlog of activities to raise MLOps maturity in the organization.
• Proactively introduce a modern, agile and automated approach to Data Science.
• Conduct internal training and presentations about ML Engineering and MLOps tools’ benefits and usage.

Required experience and qualifications:

• Good understanding of ML and AI concepts. Hands-on experience in ML model development.
• Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).
• Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit.
• Experience in CI/CD/CT pipelines implementation.
• Experience with cloud platforms – preferably AWS – would be an advantage.
• Excellent communication skills in English, both verbal and in writing.

Benefits

• Competitive salary 
• High impact and visibility role in a fast growing start-up
• Annual training allowance
• Macbook + Internet Allowance
• Paid Time Off (20 days plus national holidays)
• Health Insurance
• Flexible work opportunities
• Virtual and Onsite Team building activities