

ConfigUSA
Machine Learning Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer in Seattle, WA, with a contract length of "unknown" and a pay rate of "unknown." Key skills required include AWS services (SageMaker, Glue, Redshift), machine learning, data analysis, and model deployment.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
-
ποΈ - Date
November 7, 2025
π - Duration
Unknown
-
ποΈ - Location
On-site
-
π - Contract
Unknown
-
π - Security
Unknown
-
π - Location detailed
Seattle, WA
-
π§ - Skills detailed
#API (Application Programming Interface) #Model Deployment #"ETL (Extract #Transform #Load)" #Data Science #Lambda (AWS Lambda) #ML (Machine Learning) #Datasets #Data Processing #AWS (Amazon Web Services) #TensorFlow #Deployment #Redshift #Business Analysis #Data Engineering #Scala #Data Analysis #S3 (Amazon Simple Storage Service) #Documentation #AWS Glue #SageMaker #AWS SageMaker #Monitoring
Role description
ML Engineer
Local to Seattle , WA
We are looking for a skilled AWS ML Engineer to join our team and contribute to building data-driven solutions that enhance decision-making, optimize operations, and deliver business insights.
In this role, you will leverage AWSs advanced data and machine learning services to analyze large datasets, build predictive models, and deploy scalable machine learning solutions.
The ideal candidate will have a solid background in statistical analysis, machine learning, and data science, along with hands-on experience with AWS tools for model deployment and data processing
Key Responsibilities:
Data Analysis and Exploration Analyze large, complex datasets to extract meaningful insights and identify trends.
Perform exploratory data analysis (EDA) using AWS data processing tools.
Model Development
Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow.
ETL and Data Preparation
Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes
Machine Learning Pipeline Development
Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale.
Collaboration
Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs.
Model Deployment and Monitoring
Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics.
Use SageMaker and Lambda for model hosting and API development.
Documentation and Reporting Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.
Technical Skills:
AWS Services Hands-on experience with AWS SageMaker, Textract, Comprehend, Lambda, Glue, Redshift, and S3.
Machine Learning and Statistical Techniques Strong
ML Engineer
Local to Seattle , WA
We are looking for a skilled AWS ML Engineer to join our team and contribute to building data-driven solutions that enhance decision-making, optimize operations, and deliver business insights.
In this role, you will leverage AWSs advanced data and machine learning services to analyze large datasets, build predictive models, and deploy scalable machine learning solutions.
The ideal candidate will have a solid background in statistical analysis, machine learning, and data science, along with hands-on experience with AWS tools for model deployment and data processing
Key Responsibilities:
Data Analysis and Exploration Analyze large, complex datasets to extract meaningful insights and identify trends.
Perform exploratory data analysis (EDA) using AWS data processing tools.
Model Development
Build, train, and evaluate machine learning models using AWS services such as SageMaker, and frameworks like TensorFlow.
ETL and Data Preparation
Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes
Machine Learning Pipeline Development
Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale.
Collaboration
Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs.
Model Deployment and Monitoring
Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics.
Use SageMaker and Lambda for model hosting and API development.
Documentation and Reporting Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.
Technical Skills:
AWS Services Hands-on experience with AWS SageMaker, Textract, Comprehend, Lambda, Glue, Redshift, and S3.
Machine Learning and Statistical Techniques Strong






