Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer focusing on AI/ML pipelines, offering a 6-month contract (extendable to 1 year) with a pay rate of "unknown". Requires 5+ years of experience, strong MLOps knowledge, and familiarity with regulated environments.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
August 13, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Hybrid
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Arlington, VA
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🧠 - Skills detailed
#MLflow #Data Engineering #Microservices #Docker #AI (Artificial Intelligence) #"ETL (Extract #Transform #Load)" #Scala #Security #Transformers #ML (Machine Learning) #Deployment #Kubernetes
Role description
Data Engineer – AI/ML Pipeline Focus
Work Location: Hybrid or Remote for very strong candidates
Contract Duration: 6 months, extendable up to 1 year.
Work Experience: 5+ Years of experience
Skills & Experience Requirements
4+ years building, tuning, and deploying machine learning models in production environments.
Strong background in MLOps practices using MLflow or similar tools for model versioning, deployment, and governance.
Experience with microservices-based AI architectures and integration into operational platforms.
Proficiency in containerization (Docker, Kubernetes) and scalable inference serving.
Knowledge of explainability frameworks (e.g., SHAP, LIME) and bias detection techniques in AI systems.
Preferred Qualifications
Experience deploying AI models in regulated mission environments (healthcare, federal security, customs).
Familiarity with real-time risk scoring and decision-support integrations for government screening systems.
Hands-on use of graph transformers or hybrid rule+AI architectures.
Background in scaling AI solutions across multiple product categories or mission areas.
Data Engineer – AI/ML Pipeline Focus
Work Location: Hybrid or Remote for very strong candidates
Contract Duration: 6 months, extendable up to 1 year.
Work Experience: 5+ Years of experience
Skills & Experience Requirements
4+ years building, tuning, and deploying machine learning models in production environments.
Strong background in MLOps practices using MLflow or similar tools for model versioning, deployment, and governance.
Experience with microservices-based AI architectures and integration into operational platforms.
Proficiency in containerization (Docker, Kubernetes) and scalable inference serving.
Knowledge of explainability frameworks (e.g., SHAP, LIME) and bias detection techniques in AI systems.
Preferred Qualifications
Experience deploying AI models in regulated mission environments (healthcare, federal security, customs).
Familiarity with real-time risk scoring and decision-support integrations for government screening systems.
Hands-on use of graph transformers or hybrid rule+AI architectures.
Background in scaling AI solutions across multiple product categories or mission areas.
