Machine Learning Operations Engineer

Farmers Branch

Dallas, TX, United States

25 days ago

Role details

Contract type

Temporary to permanent

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

6 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Amazon Web Services Computer Programming Information Engineering Distributed Systems Apache Hadoop Monitoring of Systems Job Scheduling Python (Programming Language) Performance Tuning Azure Machine Learning Software Engineering Management of Software Versions

+10 more

Feature Engineering Pandas Pyspark Low Latency Apache 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.