Machine Learning Engineer

JOB 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, PostgreSQL, and other tools.
• Leverage GitOps patterns and CI/CD with tools like GitLab and GitHub.

Work Environment

• 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).

 REQUIRED QUALIFICATIONS / SKILLS

• 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 NoSQL databases.
• Experience with both Linux and Windows operating systems.
• Knowledge of CI/CD and Agile methodologies.
• Understanding of software design and system integration.

PREFERRED QUALIFICATIONS / SKILLS 

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

 RESPONSIBILITIES

• 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.

#CJ