Machine Learning Engineer

Veriday

. Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems .

Posted 10/23/2026contractCincinnati • Ohio • United StatesMid-LevelSenior💰 $70 - $85 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing scalable ML platforms and production-grade ML systems, with a strong focus on end-to-end ML lifecycle management, including deployment, monitoring, and governance. Proficient in building CI/CD pipelines and collaborating with cross-functional teams to ensure compliance with regulatory requirements.

Highest-signal resume keywords

Machine Learning EngineeringMLOpsAWS SageMakerPythonCI/CD Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringMLOpsPythonSQLJavaScalaFeature Store ArchitectureModel Registry ImplementationLinux AdministrationShell Scripting

Soft Skills

CollaborationMentoringCommunication

Tools & Technologies

AWS SageMakerAzure Machine LearningGoogle Vertex AIDockerKubernetesTerraformApache KafkaPrometheusGrafanaELK Stack

Certifications & Qualifications

AWS CertificationAzure CertificationGCP Certification

Industry Keywords

MLOps GovernanceRegulatory ComplianceData EngineeringData LakesReal-Time Inference

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformGrafanaJavaJenkinsKafkaKubernetesLinuxOpenShiftPandasPrometheusPySparkPythonRDBMSScalaShell ScriptingSparkSQLTerraform

About the role

Key responsibilities & impact

Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems
Manage the end-to-end ML lifecycle, including model development, training, validation, deployment, monitoring, versioning, experiment tracking, reproducibility, and governance
Build and maintain automated CI/CD/CT pipelines for model testing, validation, deployment, rollback, and retraining
Use infrastructure-as-code and automated environment provisioning
Design and support enterprise Feature Stores and Model Registries
Partner with data engineering to build scalable training and inference data pipelines integrated with data warehouses, data lakes, and operational sources
Develop batch and real-time inference frameworks, including low-latency event-driven scoring platforms
Implement monitoring and alerting for model performance, data quality, drift, and system health
Optimize ML systems for performance, reliability, maintainability, and cost efficiency
Ensure MLOps governance and compliance with security, privacy, risk, and regulatory requirements
Collaborate with data scientists, product owners, engineers, and business stakeholders
Mentor junior engineers and drive MLOps standards and best practices

Requirements

What you’ll need

5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering
3+ years building and supporting production ML systems
Experience implementing end-to-end ML lifecycle management, including deployment and orchestration, experiment tracking, model versioning, automated retraining, and Responsible AI/model governance
Hands-on experience operating ML workloads in AWS SageMaker, Azure Machine Learning, or Google Vertex AI
Strong proficiency in Python and SQL; Java and Scala
Experience with Feature Store architecture and Model Registry implementation
Experience with Snowflake, Databricks, IBM DB2 or equivalent RDBMS, data lakes, dbt, Spark, PySpark, and Pandas
Experience building CI/CD pipelines using Git/GitHub/GitLab, Jenkins, GitHub Actions, Azure DevOps, or equivalent
Experience with Terraform or other infrastructure-as-code tools
Experience with Docker and Kubernetes; OpenShift preferred
Linux administration and shell scripting experience
Experience with Apache Kafka and real-time inference pipelines
Experience with Prometheus, Grafana, ELK Stack, or cloud-native monitoring
Experience in regulated industries is highly desirable
Familiarity with MLflow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar platforms
Knowledge of distributed systems and software architecture patterns
Familiarity with AI/ML regulatory frameworks and model risk management practices
Relevant AWS, Azure, or GCP cloud certifications preferred

Benefits

Comp & perks

Competitive hourly rate
A collaborative and dynamic work environment
The opportunity to work on cutting-edge projects with a leading banking client
Potential contract extension