The Artificial Intelligence/Machine Learning (AI/ML) Engineer develops AI/ML algorithms cloud computing and/or heterogeneous distributed computing infrastructures to support the deployment of AI/ML applications. The AI/ML Engineer also researches the mathematical foundations and frameworks for nonlinear systems characterized by timevarying and emerging dynamics of evolving or adaptive systems. The AI/ML Engineer develops technical solutions at the leading edge of Artificial Intelligence Machine Learning Genetic Programming Computer Vision and advanced data processing filtering and fusion techniques in highperformance computing and distributed heterogeneous computing environments. The AI/ML Engineer writes parallel processing programs to deploy ML models developed by data scientists into more complex systems. The AI/ML Engineer has familiarity with stateoftheart opensource software frameworks and highperformance computing accelerators for machine learning. When conducting research the AI/ML Engineer leverages the most recent advances in statistical analysis of large data sets to advance stateoftheart automated sensor and data processing for a broad range of intelligent and sensorenabled systems.
Key Responsibilities
Design complex system architectures (e.g. highperformance computing clusters networks chipsets GPUs) based on available hardware (e.g. embedded systems cloud onpremise etc.)
Lead a team of engineers responsible for system deployment
Develop novel algorithms and methodologies
Engage with sponsors to understand and meet system requirements
Serve as the primary author on technical reports and proposals
Additional Responsibilities
Develop implement and apply machine learning algorithms and methods to support sponsor and internal research and development projects
Contribute to research reports white papers and competitive proposals
Required Minimum Qualifications
Experience with machine learning tools such as Tensorflow PyTorch and Scikitlearn
Experience in the applied Artificial Intelligence and Machine Learning (AI/ML) domain particularly in the area of Machine Learning Operations (MLOps)
Familiarity with software development and collaboration tools such as GitLab and Atlassian (Confluence Jira Bitbucket)
Knowledge and experience working with cloud computing platforms (e.g. Azure AWS GCP)
Preferred Qualifications
Active Secret Clearance
Research and evaluate AI models conduct validations manage AI projects and align with industry data quality and machine learning bestpractices
Provide feedback input and consultation on various artifacts including technical analysis of vendor products academic insights inputs to the DoD Enterprise AI strategy and technical documentation
Provide technical analysis on vendor products through market research and participation in vendor demos offering insights into their capabilities
Share expertise and keep teams informed about emerging trends on AI advancement through presentation literature summaries or curated resource list
Research and evaluate costeffective AI/ML solutions for DoD enterprise use cases.
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