{"schemaVersion":"jobsearcher.job.v1","id":"f3e87eff4dd63455f92629a2","url":"https://jobsearcher.com/jobs/f3e87eff4dd63455f92629a2","canonicalUrl":"https://jobsearcher.com/jobs/f3e87eff4dd63455f92629a2","title":"Full Stack MLOps Engineer (Databricks / ML Applications)","description":"TQUSI0682_5573 - Full Stack MLOps Engineer (Databricks / ML Applications)\r\nJob Type: Contract\r\nWork Mode: Hybrid (2 Days from office)\r\nWe are seeking an MLOps Engineer to build and maintain CI/CD pipelines for machine learning models and scripts. This role bridges the gap between data science and production engineering, ensuring ML models are deployed reliably, monitored effectively, and updated seamlessly in production environments.\r\nKey Responsibilities\r\nBuild and deploy ML applications on Databricks (end-to-end)\r\nDevelop CI/CD pipelines for ML workflows and data pipelines\r\nWork with Databricks (Delta Lake, notebooks, jobs, workflows)\r\nBuild APIs (Python/FastAPI) to serve ML models\r\nContainerize and deploy applications using Docker & Kubernetes\r\nImplement monitoring, logging, and model performance tracking\r\nCollaborate with data scientists to productionize models\r\nRequired Qualifications\r\nTechnical Skills\r\nProgramming & Scripting:\r\nPython (advanced) - Primary language for ML and automation\r\nBash/Shell scripting for automation\r\nYAML for configuration management\r\nUnderstanding of software engineering best practices\r\nCI/CD Tools:\r\nGitHub Actions, GitLab CI/CD, or Jenkins - Building automated pipelines\r\nExperience with pipeline-as-code concepts\r\nAutomated testing frameworks (pytest, unittest)\r\nContainerization & Orchestration:\r\nDocker - Container creation and management (required)\r\nContainer registries (Docker Hub, ECR, ACR, GCR)\r\nExperience with AWS, Azure, or GCP (at least one)\r\nCloud storage (S3, Blob Storage, GCS)\r\nMLOps Tools\r\nMLflow - Experiment tracking and model registry\r\nDVC (Data Version Control) - Data and model versioning\r\nWeights & Biases, Neptune.ai, or similar (nice to have)\r\nInfrastructure as Code\r\nTerraform or CloudFormation/ARM templates\r\nExperience managing infrastructure through code\r\nUnderstanding of state management\r\nVersion Control\r\nGit (advanced) - Branching strategies, merge workflows\r\nGitHub/GitLab/Bitbucket repository management\r\nML Knowledge\r\nUnderstanding of ML Workflows\r\nFamiliarity with ML model training and inference\r\nUnderstanding of model formats (pickle, ONNX, SavedModel, TorchScript)\r\nKnowledge of ML frameworks (scikit-learn, TensorFlow, PyTorch) - not required to build models, but must understand how they work\r\nAwareness of ML lifecycle (training, validation, deployment, monitoring)\r\nModel Serving\r\nFastAPI or Flask - Building REST APIs for model serving\r\nTensorFlow Serving, TorchServe, or ONNX Runtime (nice to have)\r\nUnderstanding of model optimization (quantization, pruning)\r\nMonitoring & Observability\r\nMonitoring Tools\r\nPrometheus & Grafana - Metrics and dashboards\r\nELK Stack (Elasticsearch, Logstash, Kibana) or similar for logging\r\nML-Specific Monitoring:\r\nModel drift detection (Evidently AI, Arize, WhyLabs)\r\nPerformance metrics tracking\r\nDevOps & Software Engineering\r\nBest Practices\r\nDocumentation standards\r\nSecurity best practices for ML systems\r\nTesting\r\nUnit testing, integration testing\r\nData validation and schema testing\r\nExperience Requirements\r\n3-5+ years in DevOps, MLOps, or software engineering\r\n1-2+ years specifically working with ML model deployment and CI/CD\r\nProven track record of building and maintaining production ML systems\r\nExperience with cloud platforms and containerization\r\nHands-on experience with CI/CD pipeline development\r\nJ-18808-Ljbffr","company":"Testq Technologies","rawCompany":"testq technologies","city":"Ocean Grove","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-04-21T04:54:22.205Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Full Stack MLOps Engineer (Databricks / ML Applications)","description":"TQUSI0682_5573 - Full Stack MLOps Engineer (Databricks / ML Applications)\r\nJob Type: Contract\r\nWork Mode: Hybrid (2 Days from office)\r\nWe are seeking an MLOps Engineer to build and maintain CI/CD pipelines for machine learning models and scripts. This role bridges the gap between data science and production engineering, ensuring ML models are deployed reliably, monitored effectively, and updated seamlessly in production environments.\r\nKey Responsibilities\r\nBuild and deploy ML applications on Databricks (end-to-end)\r\nDevelop CI/CD pipelines for ML workflows and data pipelines\r\nWork with Databricks (Delta Lake, notebooks, jobs, workflows)\r\nBuild APIs (Python/FastAPI) to serve ML models\r\nContainerize and deploy applications using Docker & Kubernetes\r\nImplement monitoring, logging, and model performance tracking\r\nCollaborate with data scientists to productionize models\r\nRequired Qualifications\r\nTechnical Skills\r\nProgramming & Scripting:\r\nPython (advanced) - Primary language for ML and automation\r\nBash/Shell scripting for automation\r\nYAML for configuration management\r\nUnderstanding of software engineering best practices\r\nCI/CD Tools:\r\nGitHub Actions, GitLab CI/CD, or Jenkins - Building automated pipelines\r\nExperience with pipeline-as-code concepts\r\nAutomated testing frameworks (pytest, unittest)\r\nContainerization & Orchestration:\r\nDocker - Container creation and management (required)\r\nContainer registries (Docker Hub, ECR, ACR, GCR)\r\nExperience with AWS, Azure, or GCP (at least one)\r\nCloud storage (S3, Blob Storage, GCS)\r\nMLOps Tools\r\nMLflow - Experiment tracking and model registry\r\nDVC (Data Version Control) - Data and model versioning\r\nWeights & Biases, Neptune.ai, or similar (nice to have)\r\nInfrastructure as Code\r\nTerraform or CloudFormation/ARM templates\r\nExperience managing infrastructure through code\r\nUnderstanding of state management\r\nVersion Control\r\nGit (advanced) - Branching strategies, merge workflows\r\nGitHub/GitLab/Bitbucket repository management\r\nML Knowledge\r\nUnderstanding of ML Workflows\r\nFamiliarity with ML model training and inference\r\nUnderstanding of model formats (pickle, ONNX, SavedModel, TorchScript)\r\nKnowledge of ML frameworks (scikit-learn, TensorFlow, PyTorch) - not required to build models, but must understand how they work\r\nAwareness of ML lifecycle (training, validation, deployment, monitoring)\r\nModel Serving\r\nFastAPI or Flask - Building REST APIs for model serving\r\nTensorFlow Serving, TorchServe, or ONNX Runtime (nice to have)\r\nUnderstanding of model optimization (quantization, pruning)\r\nMonitoring & Observability\r\nMonitoring Tools\r\nPrometheus & Grafana - Metrics and dashboards\r\nELK Stack (Elasticsearch, Logstash, Kibana) or similar for logging\r\nML-Specific Monitoring:\r\nModel drift detection (Evidently AI, Arize, WhyLabs)\r\nPerformance metrics tracking\r\nDevOps & Software Engineering\r\nBest Practices\r\nDocumentation standards\r\nSecurity best practices for ML systems\r\nTesting\r\nUnit testing, integration testing\r\nData validation and schema testing\r\nExperience Requirements\r\n3-5+ years in DevOps, MLOps, or software engineering\r\n1-2+ years specifically working with ML model deployment and CI/CD\r\nProven track record of building and maintaining production ML systems\r\nExperience with cloud platforms and containerization\r\nHands-on experience with CI/CD pipeline development\r\nJ-18808-Ljbffr","datePosted":"2026-04-21T04:54:22.205Z","dateModified":"2026-04-21T04:54:22.205Z","hiringOrganization":{"@type":"Organization","name":"Testq Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Ocean Grove","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"f3e87eff4dd63455f92629a2"},"url":"https://jobsearcher.com/jobs/f3e87eff4dd63455f92629a2"}}