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

Are you ready to apply Make sure you understand all the responsibilities and tasks associated with this role before proceeding.

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 xzgxqkn 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