Machine Learning Engineer: Role and Skills

2 min read
Machine Learning Engineer

In recent years, interest in Artificial Intelligence has been growing exponentially. One of the most popular areas of AI now is Machine Learning. Businesses are excited about implementing this technology in their processes, as tech professionals try to keep up with the trends, acquire new skills and join a new emerging occupation – Machine Learning Engineer.

Machine Learning is a foundation for WorkFusion’s Intelligent Automation Cloud, and Machine Learning Engineer (MLE) is one of the most technically skilled roles in implementation teams. Let’s see what they do and what skills are required from them.

Role of a Machine Learning Engineer

What does a Machine Learning Engineer do in an automation project? Their role includes a broad range of responsibilities in developing solutions, rolling them out to Production and dealing with a variety of environment challenges, including:

  • Creating training sets from customer data and enforcing their quality
  • Setting up machine learning use cases and configuring ML components
  • Designing the sequential launch of training processes
  • Transferring machine learning solutions from Development to Testing to Production, which requires deep knowledge of environments’ differences and usage strategy
  • Analysis of machine learning statistics and parameters which influence the model results
  • Post-processing implementation in cooperation with Data Analysts

In order to perform these tasks well, an MLE needs to have specific, high-demand skills.

Machine Learning Engineer skills

As we develop and deliver training for Machine Learning Engineers, we have identified this skillset needed to become a WorkFusion MLE:

  • Strong technical production Java background, including:
    • experience with code review, build tools (Maven, Jenkins), VCS (git)
    • proficiency in IDEs (Eclipse/IDEA), Atlassian stack
    • good knowledge of how to handle exceptions in Java
  • Proven developer experience with SQL:
    • knowledge of JOINs, WHERE, GROUP BY/HAVING
    • nested expressions, aggregates, normal forms
  • Knowledge of ML basics and WorkFusion’s AutoML SDK knowledge
  • Experience working with common data formats in Java: JSON, XML/HTML, DOC/DOCX, XLS/XLSX, CSV, TXT and regular expressions
  • Linux command-line experience
  • Ability to use SSH to view logs, restart Linux servers/services
  • Knowledge of OOP and design patterns

In which parts of the automation project would an MLE apply all these skills?

Here are typical ML project implementation phases and the role of an MLE in them. You can also compare this to an RPA developer role in the project.

typical project implementation phases

How to become a Machine Learning Engineer

To ensure successful delivery of every ML project, Automation Academy by WorkFusion offers Machine Learning Engineer training: a special learning path for WorkFusion partners and customers that consists of 6 courses, with a combined duration of under 4 weeks.

The Machine Learning courses cover the following topics:

  • Machine Learning basics
  • Automation of Information Extraction (IE)
  • Data Categorization
  • WorkFusion’s AutoML SDK
  • ML Engineer runbooks, best practices and more 

At the end of the learning path, trainees will deliver an actual PoC use case and then will be certified as Machine Learning Engineers.

Are you excited to become an MLE? Find out how to apply for Academy advanced courses.

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