Machine Learning Operations Engineer

Farmers Branch
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

Job 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.

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.

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