{"schemaVersion":"jobsearcher.job.v1","id":"de6729ca68dff808f902863c","url":"https://jobsearcher.com/jobs/de6729ca68dff808f902863c","canonicalUrl":"https://jobsearcher.com/jobs/de6729ca68dff808f902863c","title":"Machine Learning Operations Engineer","description":"Job Title: Machine Learning Operations Engineer\nLocation: Dallas, Texas\nType: Contract To Hire\n\nVisa : USC, GC, EAD (Only W2, No Sponsorship)\nResponsibilities\nOptimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.\nRefactor and modularize ML codebases to enhance reusability, maintainability, and performance.\nCollaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.\nIntegrate with existing model serving frameworks to support testing, deployment, and rollback processes.\nMonitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.\nContribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.\nBuild near real-time ML pipelines using Kafka and Spark Streaming.\nWork with AWS and SageMaker MLOps ecosystem.\nRequirements\n6+ years of experience in software engineering, data engineering, or MLOps roles.\nStrong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.\nDeep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.\nExperience with CI/CD pipelines and best practices in ML environments.\nHands-on experience with monitoring tools for ML pipeline health and performance.\nStrong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).\nExperience contributing to or building internal MLOps frameworks/platforms.\nFamiliarity with SLURM clusters or other distributed job schedulers.\nExposure to Kafka, Spark Streaming, or other real-time data processing technologies.\nUnderstanding of ML lifecycle management, including versioning, deployment, and drift detection.\n#M1\n#DI-CB2\n\n#L1 - KB1\n\nRef: #404-IT Pittsburgh","company":"Systemone","rawCompany":"systemone","city":"Dallas","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-08-04T21:31:44.094Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Operations Engineer","description":"Job Title: Machine Learning Operations Engineer\nLocation: Dallas, Texas\nType: Contract To Hire\n\nVisa : USC, GC, EAD (Only W2, No Sponsorship)\nResponsibilities\nOptimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.\nRefactor and modularize ML codebases to enhance reusability, maintainability, and performance.\nCollaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.\nIntegrate with existing model serving frameworks to support testing, deployment, and rollback processes.\nMonitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.\nContribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.\nBuild near real-time ML pipelines using Kafka and Spark Streaming.\nWork with AWS and SageMaker MLOps ecosystem.\nRequirements\n6+ years of experience in software engineering, data engineering, or MLOps roles.\nStrong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.\nDeep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.\nExperience with CI/CD pipelines and best practices in ML environments.\nHands-on experience with monitoring tools for ML pipeline health and performance.\nStrong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).\nExperience contributing to or building internal MLOps frameworks/platforms.\nFamiliarity with SLURM clusters or other distributed job schedulers.\nExposure to Kafka, Spark Streaming, or other real-time data processing technologies.\nUnderstanding of ML lifecycle management, including versioning, deployment, and drift detection.\n#M1\n#DI-CB2\n\n#L1 - KB1\n\nRef: #404-IT Pittsburgh","datePosted":"2026-08-04T21:31:44.094Z","dateModified":"2026-08-04T21:31:44.094Z","hiringOrganization":{"@type":"Organization","name":"Systemone","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dallas","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"de6729ca68dff808f902863c"},"url":"https://jobsearcher.com/jobs/de6729ca68dff808f902863c"}}