Machine Learning Operations Engineer

System One Holdings, LLC

$60K — $120K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 6+ years experience in software engineering, data engineering, or MLOps
  • Proficient in Python with practical knowledge of Pandas, PySpark, and PyArrow
  • Solid grasp of Hadoop ecosystem and distributed computing
  • Experience with CI/CD in machine learning environments
  • Hands-on with monitoring tools for ML pipeline performance
  • Strong team collaboration skills, especially in cross-functional teams
  • Familiar with MLOps frameworks and possibly SLURM clusters

Responsibilities

  • Optimize and maintain feature engineering pipelines on Hadoop infrastructures
  • Refactor ML codebases for better usability and performance
  • Collaborate on compute capacity planning and resource allocation
  • Integrate model serving frameworks for deployment and testing
  • Monitor and troubleshoot ML pipelines to ensure efficiency
  • Share insights and document best practices for internal ML platforms
  • Build near real-time ML pipelines with Kafka and Spark Streaming

Benefits

  • Health and welfare benefits coverage options including medical, dental, and vision
  • 401(k) plan participation
  • Life insurance and voluntary plans
  • Spending accounts for additional coverage needs
Full Job Description
Job Title: Machine Learning Operations Engineer
Location: Dallas, Texas
Type: Contract To Hire
Compensation: $60.00 - $120,000.00
Work Model: Onsite - onsite
Hours: 40.0
Security Clearance: None specified

Overview
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Responsibilities

  • 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.
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.


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.

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Ref: #404-IT Pittsburgh

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