Machine Learning Operations Engineer
System One
Dallas, United States of America
yesterday
Role details
Contract type
Temporary to permanent Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
Senior Compensation
$ 120KJob location
Dallas, United States of America
Tech stack
Amazon Web Services (AWS)
Computer Programming
Information Engineering
Distributed Systems
Hadoop
Monitoring of Systems
Job Scheduling
Python
Performance Tuning
Azure
Software Engineering
Management of Software Versions
Feature Engineering
Pandas
PySpark
Low Latency
Kafka
Spark Streaming
Slurm
Machine Learning Operations
Stream Processing
Code Restructuring
Job description
- Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.
- Refactor and modularize ML codebases to enhance reusability, maintainability, and performance.
- Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.
- Integrate with existing model serving frameworks to support testing, deployment, and rollback processes.
- Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.
- Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.
- Build near real-time ML pipelines using Kafka and Spark Streaming.
- Work with AWS and SageMaker MLOps ecosystem., System One, and its subsidiaries including Joulé and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan.
Requirements
- 6+ years of experience in software engineering, data engineering, or MLOps roles.
- Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.
- Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.
- Experience with CI/CD pipelines and best practices in ML environments.
- Hands-on experience with monitoring tools for ML pipeline health and performance.
- Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).
- Experience contributing to or building internal MLOps frameworks/platforms.
- Familiarity with SLURM clusters or other distributed job schedulers.
- Exposure to Kafka, Spark Streaming, or other real-time data processing technologies.
- Understanding of ML lifecycle management, including versioning, deployment, and drift detection.