View ML Engineer tasks

The Machine Learning Engineer (ML Engineer) is one of the most technically skilled roles. The ML Engineer background includes a variety of completed end-to-end Java-based projects and a wide experience in creating business applications, rolling out to production, and dealing with environment challenges.

What is expected from ML Engineer

The ML Engineer's tasks are as follows:

• Enforce a qualitative training set as a starting point of ML solution:
  • Perform and explain Data Analyst (DA) key functions: document labeling, analyze P/R/TP/FP/FN.
  • Identify problems with a training set and solve them by manipulating document sets.
• Improve a model based on data analysis following the WorkFusion solution workflow:
  • Analyze training results and understand what needs to be improved based on data analysis.
  • Know parameters that influence the overall model extraction quality and methods to improve it.
  • Know how to read extraction per field output statistics and what needs to be improved based on this analysis.
  • Understand typical AutoML pipeline: OCR > Labeling > OOTB Model training > AutoML SDK.
  • Possess practical skills in Search Engine settings for training (fields, experiments, time limit, max iterations).
  • Understand how to launch fixed model training or an improved training set for the previously trained model without Search Engine.
  • Be able to split a data set and a test model on a test data set.
  • Have business case implementation experience, with a delivered PoC case as a final certification task during the Automation Academy learning path.
• Use WorkFusion AutoML components as an ML solution:
  • Set up an ML Business Process in Control Tower, fine-tune a Business Process, configure AutoML in a Manual Task, perform training in Control Tower, create Bot Tasks, and design sequential launch of training processes.
  • Deploy a model to make it available via Control Tower.
  • Transfer an ML solution through DEV > QA > UAT > PROD.
  • Understand the difference between the Development and Production environments and their usage strategy.
  • Solve issues on failed training processes.
  • Configure Mesos/Marathon and ZooNavigator settings.
  • Tune OCR capabilities, distinguish OCR errors, and fix them using AutoML SDK.
  • Know and use environment health checks.
  • Apply investigation skills when training fails.
• Apply AutoML SDK when needed.
• Understand the theoretical meaning of Annotators and Feature Extractors and how they relate to model fields.
• Be able to describe the AutoML SDK flow logically.

Role in RACI matrix

RACI stands for:

• R: Responsible
• C: Consult
• A: Accountable
• I: Informed

For ML Engineer responsibilities according to the RACI matrix, refer to the table below:

Area	Responsibility
Set up OCR environment and Business Processes	I
Create a Qualification Task for a customer (SME)	I
Prepare a training set	AC (technical help to DA if required)
Analyze and improve a training set	AC
Create a training set from customer data	R (with DA)
Automate a Business Process	RA
Set up ML environment	RA
Train a model	RA
Evaluate ML results and statistics	R (with DA)
Analyze metrics and provide post-processing description	CR (with DA)
Perform post-processing implementation	RA
Generate automation report for a customer	C