MLOps Engineer

Start Date: Interview TypesSkills . Experience owning .. Visa Types Green Card, US Citiz..Client: Walmart Inc.Location: Grapevine, TX (Dallas, TX) - hybrid onsiteTitle: MLOps EngineerDuration: 6 months+, possible extensionRate: $80/hr+, depending on experienceDescriptionWe are seeking a highly skilled MLOps Engineer to support the IRAS (Item Recognition as a Service) team, a core component of the ISEE (Intelligent Store Execution Engine) platform at Sam's Club.This role is responsible for owning and evolving the end-to-end MLOps lifecycle, enabling scalable, production-grade machine learning systems that power computer vision, RFID integration, and real-time item recognition across store environments.You will operate horizontally across multiple ML products and pipelines, ensuring reliability, scalability, and rapid iteration of ML-driven capabilities that support frictionless shopping experiences.Key ResponsibilitiesMLOps Lifecycle OwnershipOwn the full ML lifecycle: data ingestion, model training, validation, deployment, monitoring, and retrainingBuild and maintain robust model pipelines for computer vision and IRAS-based item recognition systemImplement data and model monitoring (drift detection, performance degradation, alerting)Refactor and productionize data science code into scalable, reusable servicePlatform & Pipeline EngineeringDesign and operate end-to-end ML pipelines (data training evaluation deployment feedback loops)Enable model versioning, reproducibility, and governance at scaleSupport real-time and batch inference systems tied to RFID + camera fusion pipelines [Sam\'s iSEE POC | PowerPoint]CI/CD & DevOps for MLBuild and maintain CI/CD pipelines for ML workflows (model builds, testing, deployment)Automate testing, validation, and release processes for ML models and data pipelineEnsure high availability, rollback capability, and release governanceCross-Product / Horizontal SupportServe as a shared MLOps capability across multiple IRAS and ISEE initiativePartner with Data Scientists, CV/ML Engineers, and Platform teams to accelerate model delivery and adoptionStandardize tools, frameworks, and best practices across teamRequired Skills & ExperienceCore Requirements (Must Have)Proven experience owning MLOps lifecycle end-to-end in production environmentStrong Python engineering experience (building scalable services, pipelines)Hands-on expertise with CI/CD pipelines for ML systemExperience supporting multiple ML products or platforms simultaneouslyMLOps & ML ToolingDeep knowledge of:MLflow / Kubeflow (or similar orchestration frameworks)Model versioning, experiment tracking, and pipeline orchestrationData monitoring, drift detection, and model observabilityStrong understanding of machine learning fundamentals and lifecycle managementData & Cloud EcosystemExperience with:GCP (preferred), Azure, or hybrid environmentBig Data ecosystems (Databricks, Spark, distributed processing)SQL and large-scale data pipeline developmentFamiliarity with cloud-based ML infrastructure and scaling strategieEngineering & SystemProficiency in:Shell scripting and automationContainerization and orchestration (Docker, Kubernetes)Building reliable, scalable backend systems for ML inferencePreferred / Nice-to-HaveExperience with computer vision pipelines (CV/ML models, object detection, item recognition)Exposure to RFID data integration or sensor fusion systemExperience working in retail, edge + cloud hybrid environments, or real-time inference systemFamiliarity with GPU-based inference optimization and performance tuningWhat Success Looks LikeFully automated, production-grade ML pipelines supporting IRAS item recognitionReduced latency and improved reliability of model deployment and inference workflowStandardized MLOps practices across ISEE teamHigh-confidence, monitored models with continuous improvement loopTop Skills DetailsTop Skills' DetailExperience owning MLOps lifecycle, from data monitoring to refactoring data science code to building robust ML model lifecycle.Python EngineeringCI/CDExperience with MLOps driven data science outcomes and handle ML Engineering horizontal helping multiple products and initiatives.have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, SQL, Big Data, GCP, Shell scripting.Worksite Address1701 West State Highway 114,Grapevine,Texas,United States,76051Workplace TypeHybridEVPYou are getting to work with several teams that are on the cutting edge of technology for retail.Work EnvironmentFast paced, POC so like a startup, must be a self starterAdditional Skills & QualificationsMust be W2. Prefer to have them in DFW area to go to the Grapevine Sam\'s Club 1-2 times per week but not an absolute must.Business ChallengeBy executing this IRAS project, we will be able to take on more of their monarch work going into next year. It will allow their item recognition service to exceed 85-90% accuracy