Title: Senior Machine Learning Operations (MLOps) Engineer
Location: 38 Fountain Square Plaza, Cincinnati, OH – 45202
Duration: 12 Months contract
Key Responsibilities
ML Platform Engineering & Model Operations
• Design, 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 Development
• Design 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 Integration
• Design 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 & Reliability
• Implement model monitoring frameworks covering:
• Model performance
• Data quality
• Data drift
• Concept drift
• Feature drift
• System health and reliability
• Establish alerting, observability, and incident management processes.
• Develop strategies for model retraining and continuous performance improvement.
Real-Time Machine Learning Systems
• Build 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 & Compliance
• Implement 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 Leadership
• Collaborate 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.
Required Qualifications
Experience
• 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.
Required Technical Skills
Machine Learning & MLOps
• ML lifecycle management
• Model deployment and orchestration
• Experiment tracking and model versioning
• Model monitoring and observability
• Feature Store architecture
• Model Registry implementation
• Automated retraining workflows
• Responsible AI and model governance practices
Cloud Platforms
Experience with one or more:
• AWS SageMaker
• Azure Machine Learning
• Google Vertex AI
Programming
Proficiency in:
• Python (required)
• SQL (required)
• Java, Scala, or R (preferred)
Data Platforms
Experience with:
• Snowflake
• Databricks
• IBM DB2 or equivalent enterprise RDBMS platforms
• Data lakes and modern cloud storage platforms
Data Transformation
Experience with:
• dbt
• Spark
• Pandas
• PySpark
DevOps & Infrastructure
Experience with:
• Git/GitHub/GitLab
• Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools
• Terraform or infrastructure-as-code frameworks
• Linux administration
• Shell scripting
Containers & Orchestration
Experience with:
• Docker
• Kubernetes
• OpenShift (preferred)
Streaming & Real-Time Technologies
Experience with:
• Apache Kafka
• Event-driven architectures
• Real-time inference pipelines
Monitoring & Observability
Experience with:
• Prometheus
• Grafana
• ELK Stack
• Cloud-native monitoring solutions
Preferred Qualifications
• Experience implementing enterprise Feature Stores.
• Experience designing Model Registry and ML Governance frameworks.
• Experience building real-time decisioning 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.
• Reusable machine learning platform services that accelerate model development and deployment across data science teams.