Unisys
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "unknown," offering a pay rate of "unknown" and remote work. Key skills required include AWS, Python, MLflow, and experience with Domino and SageMaker. Familiarity with MLOps practices is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 3, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Reston, VA
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🧠 - Skills detailed
#GIT #Scala #Data Science #Data Pipeline #Monitoring #Data Engineering #ML Ops (Machine Learning Operations) #ML (Machine Learning) #Version Control #Deployment #MLflow #Python #AWS (Amazon Web Services) #SageMaker #Model Validation #Compliance
Role description
• We are seeking a highly skilled ML/Data Engineer to lead model development, experiment tracking, and end to end Machine Learning operations across Domino and Amazon SageMaker. This role will drive model lifecycle quality, governance alignment, and engineering excellence.
Responsibilities:
• Own the monitoring, tracking, and maintenance of ML models across Domino and SageMaker platforms.
• Implement MLflow for parameters, metrics, artifact management, and end to end lineage.
• Build and maintain scalable data pipelines for training, validation, and inference processes.
• Develop custom evaluation metrics, explainability components, and fairness/bias testing frameworks.
• Package models for deployment and support model lifecycle transitions across environments.
• Collaborate with data scientists, engineering teams, and governance stakeholders to ensure compliance and operational readiness.
Required Skills & Experience:
• Strong experience with AWS and ML engineering.
• Proficiency in Python and MLflow.
• Hands on expertise with Domino and SageMaker SDKs.
• Experience with feature engineering and scalable data pipelines.
• Knowledge of model validation, explainability, and bias/fairness tooling.
• Familiarity with Git based workflows, version control, and MLOps practices.
#LI-CGTS
#TS-3142
• We are seeking a highly skilled ML/Data Engineer to lead model development, experiment tracking, and end to end Machine Learning operations across Domino and Amazon SageMaker. This role will drive model lifecycle quality, governance alignment, and engineering excellence.
Responsibilities:
• Own the monitoring, tracking, and maintenance of ML models across Domino and SageMaker platforms.
• Implement MLflow for parameters, metrics, artifact management, and end to end lineage.
• Build and maintain scalable data pipelines for training, validation, and inference processes.
• Develop custom evaluation metrics, explainability components, and fairness/bias testing frameworks.
• Package models for deployment and support model lifecycle transitions across environments.
• Collaborate with data scientists, engineering teams, and governance stakeholders to ensure compliance and operational readiness.
Required Skills & Experience:
• Strong experience with AWS and ML engineering.
• Proficiency in Python and MLflow.
• Hands on expertise with Domino and SageMaker SDKs.
• Experience with feature engineering and scalable data pipelines.
• Knowledge of model validation, explainability, and bias/fairness tooling.
• Familiarity with Git based workflows, version control, and MLOps practices.
#LI-CGTS
#TS-3142
