Tekskills Inc.
Senior Azure AI / Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Azure AI / Machine Learning Engineer, remote for 12+ months, with a pay rate of "unknown." Key skills include Python, .Net, Azure Open AI, and experience with ML libraries and cloud data services.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 24, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Azure #ADF (Azure Data Factory) #Model Validation #ML (Machine Learning) #TensorFlow #AWS (Amazon Web Services) #AI (Artificial Intelligence) #BigQuery #GCP (Google Cloud Platform) #AWS Glue #Libraries #Databricks #Cloud #Python #Synapse #Deployment #Azure Data Factory #.Net
Role description
Job Title : Senior Azure AI / Machine Learning Engineer
Location : Redmond, WA 98052-Remote
Duration : 12+ Months
Must Have Skills
Python, .Net, Azure Open AI, Modern Azure Services
Job Description
• Strong foundation in Python, .Net, Azure Open AI and modern Azure Services Experience with ML libraries (scikit-learn, TensorFlow, ML.NET, Azure ML, etc.).
• Understanding of machine learning lifecycle: data prep → training → evaluation → deployment.
• Knowledge of statistical methods, model validation, and experiment design.
• Familiarity with cloud data services (Azure Data Factory, Synapse, Databricks, AWS Glue, GCP BigQuery).
• Ability to design clean, maintainable data structures and pipelines.
• Strong problem-solving, analytical thinking, and communication skills.
Job Title : Senior Azure AI / Machine Learning Engineer
Location : Redmond, WA 98052-Remote
Duration : 12+ Months
Must Have Skills
Python, .Net, Azure Open AI, Modern Azure Services
Job Description
• Strong foundation in Python, .Net, Azure Open AI and modern Azure Services Experience with ML libraries (scikit-learn, TensorFlow, ML.NET, Azure ML, etc.).
• Understanding of machine learning lifecycle: data prep → training → evaluation → deployment.
• Knowledge of statistical methods, model validation, and experiment design.
• Familiarity with cloud data services (Azure Data Factory, Synapse, Databricks, AWS Glue, GCP BigQuery).
• Ability to design clean, maintainable data structures and pipelines.
• Strong problem-solving, analytical thinking, and communication skills.
