Machine Learning Operations (MLOps) Engineer (Only W2)

Role: Machine Learning Operations (MLOps) Engineer

Location: Cincinnati, OH - Onsite

Pay Rate: $70-80/hour on W2, client will not consider C2C

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