ML Ops / Google Cloud Platform Data Engineer (Only W2 role)
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Patton Labs Inc., is seeking the following. Apply via Dice today!Job Title: ML Ops Engineer / Google Cloud Platform Data EngineerLocation: Hybrid – 4 Days OnsiteDuration: Long Term ContractJob DescriptionWe are seeking an experienced ML Ops Engineer / Cloud Data Engineer with strong expertise in building scalable machine learning platforms and cloud-native data pipelines on Google Cloud Platform (Google Cloud Platform). The ideal candidate will have hands-on experience supporting production ML environments, real-time streaming architectures, CI/CD automation, and large-scale data engineering initiatives.This role will support enterprise AI/ML and connected vehicle initiatives by developing and optimizing robust ML pipelines, monitoring solutions, and cloud infrastructure in an Agile environment.Required SkillsStrong hands-on experience in MLOps and Machine Learning PlatformsExpertise with Google Cloud Platform (Google Cloud Platform) including:BigQueryPub/SubKubernetesCloud StorageExperience building large-scale batch and streaming pipelines using:Apache KafkaSpark / Spark SQLAirflowMicroservices architectureStrong programming skills in:PythonSQLJava/Spark preferredExperience with:TerraformDockerGitHubTektonCI/CD pipelinesREST API development/integration experienceStrong understanding of Data Governance and Cloud ArchitectureExperience working in Agile/TDD environmentsExcellent communication and stakeholder management skillsPreferred SkillsTensorFlowTelematics / Connected Vehicle DataData ModelingCloud Infrastructure ArchitectureML Model MonitoringOpen-source contributionsGoogle Cloud Platform CertificationsExperience RequiredBachelor’s Degree required6+ years of relevant experience with Bachelor’s Degree OR4+ years with Master’s DegreeStrong experience supporting production ML/AI pipelinesResponsibilitiesBuild scalable ML and data pipelines on Google Cloud PlatformDevelop real-time and batch data processing solutionsSupport continuous learning and model monitoring frameworksOptimize ML platforms for scalability, performance, security, and costMaintain CI/CD and Infrastructure-as-Code environmentsCollaborate with cross-functional teams and business stakeholdersMonitor and troubleshoot production data pipelinesSupport AI/Agentic AI initiativesKeywordsMLOps, Google Cloud Platform, Google Cloud Platform, BigQuery, Pub/Sub, Kubernetes, Python, Spark, Kafka, Airflow, Terraform, Docker, GitHub, Tekton, CI/CD, Machine Learning, AI, REST API, SQL, Microservices, TensorFlow, Data Engineering, Cloud Architecture