Mid-level Machine Learning Engineer

Overview

Machine Learning Engineer

Location:

Huntsville, Al

Job Status:
Full-time

Clearance: TS/SCI w CI/Poly

Travel:
As needed

Astrion seeking a Machine Learning Engineer to join our analytics team working on an innovative MLOps workload leveraging cutting-edge technologies and supporting a government customer in Huntsville, Alabama.

This role will be responsible for delivering automation to key national security missions interacting with petabyte-scale data on supercomputing resources.

The ideal candidate will have a background in AI/ML model development and deployment and have experience in Python programming, handling SQL databases, and working in command line interfaces.

The team will work with technologies including:

• Open source, commercial, and government software packages such as Docker, Python, Jupiter Notebooks, Postgre
  
  SQL, and other tools.
• Leverage Git Ops patterns and CI/CD with tools like Git Lab and Git Hub.

• TS/SCI with CI Polygraph
• Degree in Computer Science, Statistics, Mathematics, Physics or another quantitative field.
• 1-3 years of experience working with ML frameworks
• Programming proficiency in Python and extensive knowledge of ML frameworks, libraries data structures, and data modeling.
• Solid understanding of the full ML development lifecycle.
• Experience working with SQL and No
  
  SQL databases.
• Experience with both Linux and Windows operating systems.
• Knowledge of CI/CD and Agile methodologies.
• Understanding of software design and system integration.

• Experience with petabyte scale data sets
• Experience with multi-INT analytics
• Experience deploying, monitoring, and scaling models in production environments

• Working conditions are normal for an office environment.
• Fast paced, deadline-oriented environment.
• May require periods of non-traditional working hours including consecutive nights or weekends (if applicable).

• Integrate ML systems with other software components, ensuring that machine learning pipelines work within the overall product architecture.
• Manage the transition from prototype to production, including setting up model deployment pipelines and monitoring solutions.
• Construct optimized data pipelines to feed ML models; run tests and experiments and document findings.
• Monitor model performance post-deployment including managing model drift, rollback, and failure scenarios.
• Write clean, testable, maintainable code in Python and other languages.