Machine Learning Ops Engineer (All Levels)

$125,500 - $196,075/Yr

Noblis - Chantilly, VA

Part-time,Full-time - Entry Level

Chantilly, VA

Professional, Scientific, and Technical Services

About the position

Noblis is seeking a Machine Learning Ops Engineer to work with federal clients in Chantilly, VA. The role focuses on developing innovative machine learning solutions to address immediate mission challenges, particularly in the area of cyber data. The engineer will collaborate with data scientists, software developers, and subject matter experts to implement best practices in data science and machine learning, while also supporting the Intel Community with hands-on cyber security experience.

Responsibilities

• Develop innovative ML models to address client mission challenges.
• Collaborate with data scientists and software developers to apply best practices in ML processes.
• Work alongside cyber analysts to develop AI/ML models for cyber data.
• Maintain model versioning systems and identify new vulnerabilities in models.
• Deploy ML models into existing tools or applications.
• Automate testing of ML models using methods like PyTest.
• Manage resources efficiently in Linux and Windows environments.

Requirements

• Bachelor's degree in a STEM field.
• Experience working with cyber data (e.g., Shodan, Censys.IO).
• Professional experience in a Data Science or Development environment within the Intel Community.
• Experience creating, evaluating, and deploying AI/ML models.
• Experience with advanced machine learning methods (clustering, regression, optimization, etc.).
• Experience testing and tuning ML models.
• Experience deploying resources to cloud environments.
• Experience with Python and ML frameworks (PyTorch, TensorFlow, Keras, Scikit).
• Experience using CUDA and NVIDIA GPU accelerated libraries.
• 3-5 years of professional work experience for Senior-Level; 5+ years for Expert-Level.

Nice-to-haves

• Experience cleaning, managing, and optimizing performance with large data volumes.
• Familiarity with industry best practices for software/hardware optimization.
• Experience with machine learning, statistical modeling, time-series forecasting, and/or geospatial analytics.
• Experience with Hadoop, Spark, or other parallel storage/computing processes.
• Experience with cloud providers (AWS, GCP, Azure).

Benefits

• Health insurance
• Life insurance
• Disability insurance
• Financial benefits
• Retirement benefits
• Paid leave
• Professional development
• Tuition assistance
• Work-life programs