Machine Learning Operations (MLOps) Engineer (Only W2)

Job Description

Role: Machine Learning Operations (MLOps) Engineer

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Location: Cincinnati, OH - Onsite

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

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Required Qualifications

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Experience

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• 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.

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• 3+ years of experience building and supporting production machine learning systems.

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• Demonstrated experience implementing end-to-end machine learning lifecycle management.

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• Experience operating ML workloads in cloud environments (AWS, Azure, or GCP).

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• Experience supporting both batch and real-time machine learning solutions.

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• Experience working with large-scale structured and unstructured data.

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Required Technical Skills

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Machine Learning & MLOps

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• ML lifecycle management

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• Model deployment and orchestration

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• Experiment tracking and model versioning

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• Model monitoring and observability

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• Feature Store architecture

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• Model Registry implementation

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• Automated retraining workflows

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• Responsible AI and model governance practices

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Cloud Platforms

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Experience with one or more:

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• AWS SageMaker

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• Azure Machine Learning

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• Google Vertex AI

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Programming

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Proficiency in:

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• Python (required)

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• SQL (required)

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• Java, Scala, or R (preferred)

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Data Platforms

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Experience with:

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• Snowflake

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• Databricks

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• IBM DB2 or equivalent enterprise RDBMS platforms

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• Data lakes and modern cloud storage platforms

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Data Transformation

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Experience with:

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• dbt

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• Spark

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• Pandas

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• PySpark

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DevOps & Infrastructure

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Experience with:

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• Git/GitHub/GitLab

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• Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools

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• Terraform or infrastructure-as-code frameworks

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• Linux administration

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• Shell scripting

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Containers & Orchestration

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Experience with:

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• Docker

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• Kubernetes

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• OpenShift (preferred)

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Streaming & Real-Time Technologies

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Experience with:

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• Apache Kafka

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• Event-driven architectures

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• Real-time inference pipelines

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Monitoring & Observability

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Experience with:

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• Prometheus

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• Grafana

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• ELK Stack

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• Cloud-native monitoring solutions

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