AI/ML Developer Skills

• Algorithms
• Artificial Intelligence
• Debugging
• Deep Learning
• Evaluation
• Generative Artificial Intelligence (AI)
• Innovation
• Linux
• Support Vector Machine
• Solaris Volume Manager
• Presentations
• Python
• Research
• Machine Learning (ML)
• Text Classification
• Open Source
• Pick
• Supervision
• Algorithms
• Artificial Intelligence
• Debugging
• Deep Learning
• Evaluation
• Generative Artificial Intelligence (AI)
• Innovation
• Linux
• Support Vector Machine
• Solaris Volume Manager
• Presentations
• Python
• Research
• Machine Learning (ML)
• Text Classification
• Open Source
• Pick
• Supervision

•

Summary Job Title:

AI/ML Developer Location:

Chevy Chase, MD ( 3 Days onsite) (Need locals of MD)

Duration:

3+

Months Summary:

We are seeking an experienced AI/ML developer to work alongside our Principal AI Architect on active machine-learning projects. This is a hands-on, individual-contributor role for someone who can pick up existing work, get it production-ready, and work independently with minimal ramp-up. The work is Python

• based in a Linux environment. The Project — Application Triage (ML Classification) Pick up an in-progress text-classification effort and take it to production. Existing datasets and several models (SVM, SetFit, GenAI for sentence classification) are already built. Scope
• Review and verify our existing code; identify and fix problems.
• Harden the models and pipeline for production.
• sound evaluation methodology — precision/recall on imbalanced data, proper train/eval
• separation.
• Conduct applied R D — read relevant research, try open-source projects, evaluate results, and present findings to business users on how well different approaches meet their needs. A near-term goal is evaluating algorithms for assessing a paper's innovation, transformativeness, scientific rigor, and impact. This work is guided by the Principal AI Architect. Required
• Strong applied experience with both traditional machine learning and deep learning for text
• classification.
• Solid grasp of model evaluation methods and their common pitfalls.
• Able to read, debug, and improve existing ML code independently.
• Comfortable doing applied research — reading papers, trying open-source tools, and evaluating them — and clearly presenting results to non-technical business users. Additional Requirements
• Proficient in Python; comfortable working in a Linux environment.
• Able to work independently on well-scoped tasks with minimal supervision.
• Production-minded — writes maintainable, testable code, not just notebooks.

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