Mid-level Machine Learning Engineer

Astrion; Huntsville (AL); On-site; USD 90,000 - 130,000; Full time

Job summary

Astrion seeks a Machine Learning Engineer to join our analytics team in Huntsville, AL, delivering end-to-end ML solutions for a government customer. You will build and deploy AI/ML models, develop data pipelines, and collaborate with teams across data engineering and software development.

The role requires Python expertise, ML framework knowledge, and experience with SQL/NoSQL on Linux and Windows. Expect a fast-paced environment with opportunities to impact national security missions.

Qualifications

• TS/SCI with CI Polygraph.
• Degree in Computer Science, Statistics, Mathematics, Physics or another quantitative field.
• 1-3 years of experience with ML frameworks.
• Proficiency in Python; experience with ML libraries.
• Experience with SQL and NoSQL databases.
• Experience with Linux and Windows operating systems.
• Knowledge of CI/CD and Agile methodologies.
• Understanding of software design and system integration.

Responsibilities

• Integrate ML systems with other software components within the overall product architecture.
• Transition from prototype to production; set up deployment pipelines and monitoring solutions.
• Construct optimized data pipelines to feed ML models; run tests and document findings.
• Monitor model performance post-deployment including drift, rollback, and failure handling.
• Write clean, testable code in Python and related languages.

Skills

Python

ML frameworks

SQL

NoSQL

CI/CD

Agile

Linux

Windows

Data modeling

Education

Degree in Computer Science/Statistics/Mathematics/Physics or quantitative field

Tools

Docker

Jupyter Notebooks

PostgreSQL

GitLab

GitHub

Job description

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.