MLOps Engineer: Build Scalable ML Platforms

Rocket; Detroit (MI); Hybrid; USD 107,000 - 242,000; Full time

Benefits offered by this job

Health benefits

401K retirement plan

Paid time off

Job summary

Rocket Mortgage, a Detroit-based company, seeks an MLOps Engineer to design platforms that enable automated data-driven decision making and scalable machine learning workflows from data collection to model inference.

You will collaborate with data scientists, build services to run simulations and serve models, host trained artifacts, and develop data pipelines with monitoring tools to ensure performance and governance across teams.

Qualifications

• Master’s degree or equivalent experience in relevant field.
• Experience with ML libraries such as Scikit-learn, TensorFlow, Keras, or similar.

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.
• 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.

Skills

ML libraries

Java

Python

Spark

AWS/Azure

Big data

Education

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

Ph.D. in software development, computer science, algorithm design, artificial intelligence, or machine learning

Tools

Hadoop ecosystem

TensorFlow

Scikit-learn

Keras

Caffe

Job description

Rocket Mortgage, a Detroit-based company, seeks an MLOps Engineer to design platforms that enable automated data-driven decision making and scalable machine learning workflows from data collection to model inference.

You will collaborate with data scientists, build services to run simulations and serve models, host trained artifacts, and develop data pipelines with monitoring tools to ensure performance and governance across teams.