Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer – AWS, offering a 12+ month contract in Malvern, PA. Key skills include AWS expertise, ML & AI proficiency, Python, SQL, and MLOps experience. Data engineering knowledge is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 17, 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
United States
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🧠 - Skills detailed
#Lambda (AWS Lambda) #Docker #Redshift #Data Pipeline #ML (Machine Learning) #Data Engineering #Cloud #Deep Learning #FastAPI #Unsupervised Learning #PyTorch #Data Science #EC2 #SageMaker #TensorFlow #SQL (Structured Query Language) #Spark (Apache Spark) #Model Deployment #AI (Artificial Intelligence) #PySpark #Athena #Programming #Supervised Learning #Data Processing #Python #MLflow #Deployment #AWS (Amazon Web Services)
Role description
Job Title: Senior Machine Learning Engineer – AWS
Location: Malvern, PA (Onsite/Hybrid as per client requirements)
Duration: 12+ Months Contract
Qualifications
• Strong AWS expertise: SageMaker, Bedrock, Glue, Lambda, EMR, Athena, EC2, Redshift.
• ML & AI proficiency: Supervised/unsupervised learning, deep learning, and large language models (LLMs).
• Programming skills: Python, SQL, PySpark; frameworks such as Scikit-learn, TensorFlow, PyTorch.
• MLOps experience: CI/CD pipelines, model deployment with MLFlow, containerization with Docker, and serving with FastAPI.
• Data engineering knowledge: Feature engineering, data pipelines, and large-scale data processing in cloud environments.
• Strong problem-solving skills and ability to work in a collaborative environment.
Job Title: Senior Machine Learning Engineer – AWS
Location: Malvern, PA (Onsite/Hybrid as per client requirements)
Duration: 12+ Months Contract
Qualifications
• Strong AWS expertise: SageMaker, Bedrock, Glue, Lambda, EMR, Athena, EC2, Redshift.
• ML & AI proficiency: Supervised/unsupervised learning, deep learning, and large language models (LLMs).
• Programming skills: Python, SQL, PySpark; frameworks such as Scikit-learn, TensorFlow, PyTorch.
• MLOps experience: CI/CD pipelines, model deployment with MLFlow, containerization with Docker, and serving with FastAPI.
• Data engineering knowledge: Feature engineering, data pipelines, and large-scale data processing in cloud environments.
• Strong problem-solving skills and ability to work in a collaborative environment.
