Machine Learning Engineer

ConfigUSA; Seattle (WA); On-site; USD 100,000 - 130,000; Part time

Job summary

A technology consulting firm in Seattle is seeking a skilled AWS ML Engineer to join their team. The candidate will leverage AWS services to build predictive models and scalable machine learning solutions. Key responsibilities include data analysis, model training, and deployment using AWS tools. This is a mid-senior level contract position, ideal for those with a strong background in machine learning and data science.

Qualifications

• Solid background in statistical analysis, machine learning and data science.
• Hands-on experience with AWS tools for model deployment and data processing.

Responsibilities

• Analyze large, complex datasets to extract meaningful insights.
• Perform exploratory data analysis using AWS tools.
• Build, train, and evaluate machine learning models using AWS services.
• Work with AWS Glue and Redshift for data preprocessing.
• Develop end-to-end machine learning pipelines on AWS.
• Deploy models to production and set up monitoring systems.
• Document models, processes, and findings for clear communication.

Skills

AWS SageMaker

Data Analysis

Machine Learning

Statistical Analysis

AWS Glue

AWS Lambda

ETL

Tools

TensorFlow

AWS Textract

AWS Comprehend

AWS Redshift

AWS S3

Job description

Accounts Manager @ ConfigUSA | Client Interactions

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.
• 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.
• Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale.
• 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 grasp of ML algorithms, statistical methods, and data science best practices.

Seniority level

• Mid-Senior level

Employment type

• Contract

Job function

• Consulting, Information Technology, and Business Development

Industries

• IT Services and IT Consulting, Business Consulting and Services, and Software Development