Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer with 9+ years of experience, located in CA or AK. The contract length is unspecified, and the pay rate is also unspecified. Key skills required include Machine Learning, MLOps, Python/R, and experience with ML pipelines and web service integration.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
440
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🗓️ - Date discovered
June 6, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
California, United States
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🧠 - Skills detailed
#Agile #ML (Machine Learning) #Scripting #SQL (Structured Query Language) #Shell Scripting #Statistics #Monitoring #Scala #Batch #PySpark #Deployment #API (Application Programming Interface) #Python #MLflow #PyTorch #Data Science #Spark (Apache Spark) #R #GCP (Google Cloud Platform) #FastAPI #Big Data #Flask
Role description
NEED 9+ years of Experienced candidate and Local only to CA or AK
Top Skills' Details
Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
Experience in scaling infrastructure to support high-throughput data-intensive applications using Scala/PySpark/GPU
Worked on integrating ML models with webservices using FastAPI or Flask.
Secondary Skills - Nice to Haves
• scala
• pyspark
• gpu
• fastapi
• flask
Job Description
Machine Learning Engineer is responsible for building scalable end-to-end data science solutions.
Build ML and statistics driven models and continuous model monitoring workflows.
Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust ML model lifecycle.
Scale and deploy holistic machine learning solutions after successful prototyping.
Additional Skills & Qualifications
Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects.
Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
Experience in scaling infrastructure to support high-throughput data-intensive applications using Scala/PySpark/GPU
Worked on integrating ML models with webservices using FastAPI or Flask.
Business Drivers/Customer Impact
The key business challenge mentioned in the meeting is the need for ML engineering resources with experience in building ML pipelines, taking models to production, and working on batch and real-time API deployments.
Why is the position open(provide details)
Large retailer in need of ML engineers to assist in building ML pipelines and taking models to productions. Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects. Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
NEED 9+ years of Experienced candidate and Local only to CA or AK
Top Skills' Details
Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
Experience in scaling infrastructure to support high-throughput data-intensive applications using Scala/PySpark/GPU
Worked on integrating ML models with webservices using FastAPI or Flask.
Secondary Skills - Nice to Haves
• scala
• pyspark
• gpu
• fastapi
• flask
Job Description
Machine Learning Engineer is responsible for building scalable end-to-end data science solutions.
Build ML and statistics driven models and continuous model monitoring workflows.
Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust ML model lifecycle.
Scale and deploy holistic machine learning solutions after successful prototyping.
Additional Skills & Qualifications
Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects.
Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
Experience in scaling infrastructure to support high-throughput data-intensive applications using Scala/PySpark/GPU
Worked on integrating ML models with webservices using FastAPI or Flask.
Business Drivers/Customer Impact
The key business challenge mentioned in the meeting is the need for ML engineering resources with experience in building ML pipelines, taking models to production, and working on batch and real-time API deployments.
Why is the position open(provide details)
Large retailer in need of ML engineers to assist in building ML pipelines and taking models to productions. Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects. Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.
