AI/ML Engineer
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AI/ML Engineer
Location
Boston, Massachusetts, United States
Experience
Mid
Posted
Jul 18, 2026
Apply by
August 17, 2026
Applicants
0
Early applicantEasy applyContractWork from Office
Job Description
AI/ML Engineer
Atlanta, GA (On-site)
Contract-2-Hire
### Job Purpose
### 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 time-varying 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 high-performance 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 state-of-the-art, open-source software frameworks and high-performance 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 state-of-the-art automated sensor and data processing for a broad range of intelligent and sensor-enabled systems.
### Key Responsibilities
- Design complex system architectures (e.g., high-performance computing clusters, networks, chipsets, GPUs) based on available hardware (e.g., embedded systems, cloud, on-premise, 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 Scikit-learn
- 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 best-practices
- 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 cost-effective AI/ML solutions for DoD enterprise use cases.
Key Responsibilities
- Design complex system architectures for high-performance computing clusters, networks, and chipsets.
- Lead a team of engineers responsible for system deployment.
- Develop novel algorithms and methodologies for AI/ML applications.
- Engage with sponsors to understand and meet system requirements.
- Serve as the primary author on technical reports and proposals.
- Develop, implement, and apply machine learning algorithms to support research and development projects.
- Contribute to research reports, white papers, and competitive proposals.
Skills Required
TensorflowPyTorchScikit-learnMachine Learning Operations (MLOps)GitLabAtlassianConfluenceJiraBitbucketAzureAWSGCPCloud ComputingHigh-Performance ComputingDistributed ComputingLeadershipCommunicationProblem SolvingAI Model EvaluationAI Project ManagementVendor Product AnalysisMarket ResearchTechnical DocumentationConsultationPresentation SkillsKnowledge Sharing
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