Machine Learning Operations Engineer
Job Title: Machine Learning Operations Engineer
Location: Dallas, Texas
Type: Contract To Hire
Visa : USC, GC, EAD (Only W2, No Sponsorship)
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.
- 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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