MLOps Engineer

Rocket Companies•Detroit, MI

2d•$107,000 - $241,500

About The Position

As a MLOps Engineer, you will design and develop the platforms and frameworks that facilitate automated data-driven decision-making, gather data, and determine statistical algorithms and models that a system can use to learn from experience, predict outcomes, and make decisions. About the role Collaborate with data scientists to develop algorithms and tools for training and running simulations Build services to interact with machine learning models through simulations Participate in code reviews to ensure code quality and share best practices Develop services that host the trained models and work with other application teams to integrate them into business processes Gather and analyze large datasets and develop data model pipelines Develop algorithms that drive automated data-driven decision-making Build the tools for monitoring the performance of machine learning applications

Requirements

Master’s degree in software development, computer science, algorithm design, artificial intelligence, or machine learning or equivalent experience

Nice To Haves

1 year of experience in machine learning and using libraries such as Scikit-learn, TensorFlow, Caffe, Keras, etc.
1 year of experience working with large datasets, structured and unstructured
1 year of programming experience, including Java, or Python
1 year of experience with the Hadoop ecosystem (Apache Hive, Pig, HBase and Kafka)
1 year of experience with distributed computing platforms, such as Spark, and user interface frameworks, such as Angular or React
1 year of experience with cloud computing providers such as AWS or Azure
Ph.D. in software development, computer science, algorithm design, artificial intelligence, or machine learning or equivalent experience
Proficiency in the Microsoft Office suite
Strong object-oriented programming skills, including proficiency in Java, Scala, C/C++ or Python
Knowledge of big data

Responsibilities

Collaborate with data scientists to develop algorithms and tools for training and running simulations
Build services to interact with machine learning models through simulations
Participate in code reviews to ensure code quality and share best practices
Develop services that host the trained models and work with other application teams to integrate them into business processes
Gather and analyze large datasets and develop data model pipelines
Develop algorithms that drive automated data-driven decision-making
Build the tools for monitoring the performance of machine learning applications