Machine Learning Ops Engineer at General Atomics Intelligence
About the role General Atomics North Point Defense, a division of General Atomics Intelligence, is looking for an MLOps Engineer to manage the full machine learning lifecycle from initial research through production deployment. This position focuses on applying software engineering principles to ML systems, ensuring models are scalable, dependable, and consistently perform well in operational settings. You will work alongside data scientists, software engineers, and system engineers to deliver AI and ML solutions that support tactical and national defense intelligence missions.
Key facts
Location: Rome, NY
Engagement: Full-time
Salary: $81,000 to $141,533
What you'll do
Containerize machine learning models using Docker and deploy them into production environments where they will support real-time intelligence operations for defense applications.
Architect and build complete ML pipelines that handle data ingestion, model training, evaluation processes, and deployment workflows to ensure smooth transitions from development to production.
Establish and maintain monitoring systems and logging infrastructure for deployed models, tracking key performance indicators including accuracy metrics, response latency, and resource consumption patterns.
Partner with data scientists to move experimental models into production-ready states, ensuring research prototypes are transformed into reliable operational systems.
Work with software engineers to embed ML models into larger application frameworks, ensuring proper integration with existing system architectures and data flows.
Coordinate with system engineers to make optimal use of available hardware resources including CPUs, FPGAs, and GPUs, tuning deployments to maximize computational efficiency and throughput.
Implement continuous integration and continuous delivery practices specifically tailored for machine learning workflows, automating testing, validation, and deployment processes.
Support the development of autonomous signal processing and data dissemination solutions that provide actionable intelligence to end users in defense contexts.
Engage directly with end users during development and implementation phases to ensure solutions meet platform-specific and site-specific operational requirements.
Requirements
A bachelor's degree in computer science, engineering, mathematics, or a related technical field from an accredited institution is typically required. Equivalent professional experience as a machine learning engineer may be considered in place of formal education.
Strong proficiency in Python programming, with the ability to write clean, maintainable code for ML systems and supporting infrastructure.
Solid understanding of machine learning fundamentals and hands-on experience with frameworks such as PyTorch, which is preferred, or TensorFlow.
Practical experience using Docker to package and deploy applications in containerized environments.
Familiarity with model optimization tools including TensorRT, ONNX, and OpenVINO for improving inference performance and deployment efficiency.
Proficiency working in Linux command line environments for development, debugging, and system administration tasks.
Ability to obtain and maintain a Department of Defense security clearance is mandatory for this position.
Nice to have - Experience with C++ programming, which adds value when working on performance-critical components or integrating with existing codebases.
Background working with FPGA or GPU acceleration for machine learning inference workloads.
Prior experience in defense or intelligence community environments.
Skills & tools
Python as the primary development language for ML pipelines and automation scripts.
PyTorch or TensorFlow for building, training, and deploying machine learning models.
Docker for containerization and consistent deployment across development and production environments.
TensorRT, ONNX, and OpenVINO for model optimization and efficient inference on various hardware targets.
Linux command line tools for system interaction, scripting, and troubleshooting.
CI/CD tooling and practices applied to machine learning workflows.
Monitoring and logging frameworks for tracking deployed model performance.
Hardware optimization techniques for CPU, FPGA, and GPU resources.
Practical notes
Security clearance is a firm requirement. Candidates must be able to obtain and maintain DoD clearance to work on classified defense programs.
This role involves close collaboration with end users in defense settings, so expect direct engagement with operational stakeholders throughout the development process.
General Atomics Intelligence operates as a trusted defense industry partner, delivering solutions that support both tactical field operations and national-level intelligence priorities.
The company welcomes applicants from diverse backgrounds and experiences.
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or continue with email
About the role General Atomics North Point Defense, a division of General Atomics Intelligence, is looking for an MLOps Engineer to manage the full machine learning lifecycle from initial research through production deployment. This position focuses on applying software engineering principles to ML systems, ensuring models are scalable, dependable, and consistently perform well in operational settings. You will work alongside data scientists, software engineers, and system engineers to deliver AI and ML solutions that support tactical and national defense intelligence missions.
Key facts
Location: Rome, NY
Engagement: Full-time
Salary: $81,000 to $141,533
What you'll do
Containerize machine learning models using Docker and deploy them into production environments where they will support real-time intelligence operations for defense applications.
Architect and build complete ML pipelines that handle data ingestion, model training, evaluation processes, and deployment workflows to ensure smooth transitions from development to production.
Establish and maintain monitoring systems and logging infrastructure for deployed models, tracking key performance indicators including accuracy metrics, response latency, and resource consumption patterns.
Partner with data scientists to move experimental models into production-ready states, ensuring research prototypes are transformed into reliable operational systems.
Work with software engineers to embed ML models into larger application frameworks, ensuring proper integration with existing system architectures and data flows.
Coordinate with system engineers to make optimal use of available hardware resources including CPUs, FPGAs, and GPUs, tuning deployments to maximize computational efficiency and throughput.
Implement continuous integration and continuous delivery practices specifically tailored for machine learning workflows, automating testing, validation, and deployment processes.
Support the development of autonomous signal processing and data dissemination solutions that provide actionable intelligence to end users in defense contexts.
Engage directly with end users during development and implementation phases to ensure solutions meet platform-specific and site-specific operational requirements.
Requirements
A bachelor's degree in computer science, engineering, mathematics, or a related technical field from an accredited institution is typically required. Equivalent professional experience as a machine learning engineer may be considered in place of formal education.
Strong proficiency in Python programming, with the ability to write clean, maintainable code for ML systems and supporting infrastructure.
Solid understanding of machine learning fundamentals and hands-on experience with frameworks such as PyTorch, which is preferred, or TensorFlow.
Practical experience using Docker to package and deploy applications in containerized environments.
Familiarity with model optimization tools including TensorRT, ONNX, and OpenVINO for improving inference performance and deployment efficiency.
Proficiency working in Linux command line environments for development, debugging, and system administration tasks.
Ability to obtain and maintain a Department of Defense security clearance is mandatory for this position.
Nice to have - Experience with C++ programming, which adds value when working on performance-critical components or integrating with existing codebases.
Background working with FPGA or GPU acceleration for machine learning inference workloads.
Prior experience in defense or intelligence community environments.
Skills & tools
Python as the primary development language for ML pipelines and automation scripts.
PyTorch or TensorFlow for building, training, and deploying machine learning models.
Docker for containerization and consistent deployment across development and production environments.
TensorRT, ONNX, and OpenVINO for model optimization and efficient inference on various hardware targets.
Linux command line tools for system interaction, scripting, and troubleshooting.
CI/CD tooling and practices applied to machine learning workflows.
Monitoring and logging frameworks for tracking deployed model performance.
Hardware optimization techniques for CPU, FPGA, and GPU resources.
Practical notes
Security clearance is a firm requirement. Candidates must be able to obtain and maintain DoD clearance to work on classified defense programs.
This role involves close collaboration with end users in defense settings, so expect direct engagement with operational stakeholders throughout the development process.
General Atomics Intelligence operates as a trusted defense industry partner, delivering solutions that support both tactical field operations and national-level intelligence priorities.
The company welcomes applicants from diverse backgrounds and experiences.
#J-18808-Ljbffr
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