Overview
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.
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