Senior Machine Learning Operations (MLOps) Engineer

Senior Machine Learning Operations (MLOps) Engineer - 1+ Year Contract thru Dec 31, 2027Onsite 4 days a week in Downtown, Cincinnati Ohio 45202Pay Rate: $70/hr - $80/hr W-2

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CBTS is searching for a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems. This role will focus on enabling end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement.

The ideal candidate has strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable.

Responsibilities:ML Platform Engineering & Model OperationsDesign, develop, deploy, and manage production-grade machine learning solutions.Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring.Implement best practices for model versioning, experimentation tracking, governance, and reproducibility.Develop frameworks to support both batch and real-time inference workloads.Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.MLOps Pipeline DevelopmentDesign and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows.Automate model testing, validation, deployment, rollback, and retraining processes.Establish infrastructure-as-code practices and automated environment provisioning.Build operational tooling to support model lifecycle management across development, testing, and production environments.Feature Store & Data Engineering IntegrationDesign and support enterprise feature stores to improve feature reuse, consistency, and model governance.Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources.Develop scalable data pipelines supporting both training and inference workloads.Optimize data availability, quality, lineage, and observability.Production Monitoring & ReliabilityImplement model monitoring frameworks covering:Model performanceData qualityData driftConcept driftFeature driftSystem health and reliabilityEstablish alerting, observability, and incident management processes.Develop strategies for model retraining and continuous performance improvement.Real-Time Machine Learning SystemsBuild and support low-latency scoring and prediction platforms.Develop event-driven machine learning architectures using streaming technologies.Partner with business and engineering teams to enable near real-time decisioning capabilities.Governance, Security & ComplianceImplement MLOps governance standards and controls.Ensure compliance with enterprise security, privacy, risk, and regulatory requirements.Support model auditability, explainability, lineage tracking, and documentation.Partner with risk, compliance, and cybersecurity teams to maintain operational standards.Collaboration & Technical LeadershipCollaborate closely with data scientists, analytics teams, product owners, and software engineers.Translate business requirements into scalable technical solutions.Mentor junior engineers and contribute to MLOps standards, patterns, and best practices.Drive adoption of modern machine learning engineering methodologies across the organization.

Requirements:5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.3+ years of experience building and supporting production machine learning systems.Demonstrated experience implementing end-to-end machine learning lifecycle management.Experience operating ML workloads in cloud environments (AWS, Azure, or GCP).Experience supporting both batch and real-time machine learning solutions.Experience working with large-scale structured and unstructured data.Machine Learning & MLOpsML lifecycle managementModel deployment and orchestrationExperiment tracking and model versioningModel monitoring and observabilityFeature Store architectureModel Registry implementationAutomated retraining workflowsResponsible AI and model governance practicesCloud Platforms Experience with one or more:AWS SageMakerAzure Machine LearningGoogle Vertex AIProgramming Proficiency in:Python (required)SQL (required)Java, Scala, or R (preferred)Data Platforms Experience with:SnowflakeDatabricksIBM DB2 or equivalent enterprise RDBMS platformsData lakes and modern cloud storage platformsData Transformation Experience with:dbtSparkPandasPySparkDevOps & Infrastructure Experience with:Git/GitHub/GitLabJenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD toolsTerraform or infrastructure-as-code frameworksLinux administrationShell scriptingContainers & Orchestration - Experience with:DockerKubernetesOpenShift (preferred)Streaming & Real-Time Technologies - Experience with:Apache KafkaEvent-driven architecturesReal-time inference pipelinesMonitoring & Observability - Experience with:PrometheusGrafanaELK StackCloud-native monitoring solutions
Preferred QualificationsExperience implementing enterprise Feature Stores.Experience designing Model Registry and ML Governance frameworks.Experience building real-time decision-making or scoring platforms.Experience supporting credit risk, fraud, customer analytics, recommendation systems, or predictive analytics use cases.Experience with MLFlow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar MLOps platforms.Knowledge of software architecture patterns and distributed systems.Familiarity with AI/ML regulatory frameworks and model risk management practices.Relevant cloud certifications (AWS, Azure, GCP) preferred.
Desired Outcomes - The engineer will help establish and mature enterprise MLOps capabilities, including:Feature Store implementation and adoption.Model Registry and governance processes.Automated CI/CD/CT pipelines.Real-time model deployment and scoring.Enterprise machine learning monitoring and observability. xzbcwsv Reusable machine learning platform services that accelerate model development and deployment across data science teams.