Machine Learning Engineer

Published on Aug 17, 2021
Capital Recruiters

An Outsource company with offices in Kyiv, Ukraine; Lviv, Ukraine; Kharkiv, Ukraine.

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3-5 years
Job Type:
Data Engineering
Deep Learning
Data Science & AI

What You Will Do Responsibilities

Collaborate with robotics and automation specialists, mechanical and quality engineers to apply machine learning to industrial problems and situations
Seek opportunities in the production and development processes to utilize deep learning, algorithms and other machine learning tools for improvements

Implementation of machine learning (ML) and operations research (OR) tools, such as classical regression, classification, as well as neural networks and various optimization models for a wide range of prescriptive/predictive applications in dynamic production environments

Develop a toolkit to guide application of machine learning tools combined with statistical tools for common engineers

Assemble large data sets for analysis either through direct SQL-based querying or development of scripts and code-modules to collate distributed and disparate data sources

Analyze huge amounts of data-identifying anomalies (pattern detection) and variabilities in a measure of interest

Develops software components in Python, R and/or C/C++/ Objective-C towards roll-out of a data automation system Qualifications

What You Should Bring

4+ years of shown hands-on experience with design, implementation and application of ML/AI/Deep Learning and OR solutions and techniques to build models that solve real problems.
2+ years hands-on experience in optimization modeling, simulation and analysis with Python or Matlab.

Experience analyzing machine data (sensors, downtime log, machine states, etc) for IoT & predictive maintenance applications.

Experience applying deep learning frameworks, such as PyTorch/ Torch, TensorFlow, Keras to real-world applications that solve problems.

Knowledge of validated approaches for scale-ability, productionalizing models and implementing machine learning applied to expansive and diverse datasets (storage GPUs, techniques for deep learning at scale).

Strong software development skills with proficiency in Python.

Experienced user of machine learning and statistical-analysis libraries, such as GraphLab Create, scikit-learn, scipy, and NLTK.

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