G2001 - Machine Learning Engineer; STAR

Job Description Are you an elite Machine Learning Engineer ready to push the boundaries of Large Language Models (LLMs) and advanced AI systems in support of critical national security missions? Janus Soft is seeking a Machine Learning Subject Matter Expert to lead the evaluation, fine-tuning, and validation of state-of-the-art AI capabilities in McLean, VA.

In this pivotal role, you will design rigorous experimentation frameworks and verification test benches to evaluate LLM-based architectures, with an emphasis on Retrieval-Augmented Generation (RAG) testing and validation. Leveraging GPU-accelerated computing and deep learning frameworks (PyTorch, Tensor Flow, Keras), you will tune neural networks on custom domain datasets, benchmark performance metrics against baseline standards, and deploy resilient models for semantic search and Natural Language Processing (NLP).

If you thrive at the intersection of applied AI research, rigorous testing methodologies, and mission-focused delivery, Janus Soft offers the elite environment and compensation package to support your career growth.

Clearance:
Active TS/SCI with Full Scope Polygraph (Strict Requirement).

RAG & LLM Validation:
Demonstrated experience in experiment design and execution to test, evaluate, and validate LLM-based systems and architectures, specifically including Retrieval-Augmented Generation (RAG) testing against established benchmarks.

Neural Network Tuning:
Proven experience fine-tuning neural networks and Large Language Models (LLMs) on custom datasets and applying outputs to specialized operational use cases.

Deep Learning Frameworks:
In-depth professional or academic experience with deep learning frameworks such as PyTorch , Tensor Flow , or Keras .

Accelerated Computing:
Hands-on experience leveraging GPUs for accelerated computing, model training, and inference.

Programming & Model Development:
Strong proficiency in Python or R for developing AI/ML models and ensembles, including feature engineering, data preprocessing, library selection, and model performance analysis.

Relational Databases & SQL:
Advanced proficiency with SQL , including common table expressions (CTEs), set operations, aggregate functions, and nested subqueries.

Version Control & CI/CD:
Hands-on experience utilizing version control and automation tools such as Git Hub and Jenkins .

Demonstrated experience utilizing the Hugging Face Transformers library and ecosystem.

Experience in the design and development of novel benchmarks to quantify and measure LLM performance.

Experience with semantic search architectures and vector search applications.

Familiarity with Explainable AI ( XAI ) methodologies and Open Neural Network Exchange ( ONNX ).

Experience designing verification and validation (V&V) test benches for AI systems.

Demonstrated capability in text classification and topic modeling utilizing scikit-learn and deep learning techniques.

Experience with cloud computing architectures (e.g., AWS) and lightweight web frameworks such as Flask .

Data visualization and reporting experience with Tableau to communicate methodological approaches and model outcomes to stakeholders.