

Brillfy Technology Inc
Data/Modeling Engineer V (MLOps / ML Engineer)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data/Modeling Engineer V (MLOps / ML Engineer) on a 12+ month contract in Reston, VA (Hybrid). Key skills include AWS, MLOps, Python, MLflow, and experience with data pipelines.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
560
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🗓️ - Date
April 4, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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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
#Spark (Apache Spark) #SQL (Structured Query Language) #ML (Machine Learning) #SageMaker #Monitoring #Airflow #Data Pipeline #Deployment #Scala #Model Evaluation #Model Validation #Python #Compliance #MLflow #AWS (Amazon Web Services)
Role description
Job Title: Data/Modelling Engineer V (MLOps / ML Engineer)
Duration: 12+ Months Contract
Location: Reston, VA (Hybrid – 3 Days Onsite)
Interview Type: In-Person
Job Description:
Key Responsibilities
• Manage ML model lifecycle (training → deployment → monitoring)
• Implement MLflow for experiment tracking & lineage
• Build scalable data pipelines (training + inference)
• Work with Domino & SageMaker SDKs
• Develop model evaluation, explainability & bias frameworks
• Ensure governance, compliance, and production readiness
Required Skills
• Strong AWS + MLOps + ML Engineering
• Hands-on Python + MLflow
• Experience with Domino + SageMaker
• Data pipelines (Spark / Airflow / Hive preferred)
• Knowledge of model validation, fairness, explainability
• Strong SQL + data modelling
Job Title: Data/Modelling Engineer V (MLOps / ML Engineer)
Duration: 12+ Months Contract
Location: Reston, VA (Hybrid – 3 Days Onsite)
Interview Type: In-Person
Job Description:
Key Responsibilities
• Manage ML model lifecycle (training → deployment → monitoring)
• Implement MLflow for experiment tracking & lineage
• Build scalable data pipelines (training + inference)
• Work with Domino & SageMaker SDKs
• Develop model evaluation, explainability & bias frameworks
• Ensure governance, compliance, and production readiness
Required Skills
• Strong AWS + MLOps + ML Engineering
• Hands-on Python + MLflow
• Experience with Domino + SageMaker
• Data pipelines (Spark / Airflow / Hive preferred)
• Knowledge of model validation, fairness, explainability
• Strong SQL + data modelling






