Machine Learning Engineer

Ardent Principles, Inc.•,

12d•$130,000 - $260,000•Onsite

About The Position

We are seeking an experienced Machine Learning Engineer to support the development, tuning, validation, and deployment of advanced machine learning and Large Language Model (LLM) solutions. The role calls for hands-on experience with deep learning frameworks, experimental design and model evaluation, GPU-based computing, and RAG testing. The position is classified as SME and requires an active TS/SCI with Full Scope Polygraph. It is full-time and onsite in Northern Virginia, with consultants expected to be available and working onsite during core hours.

Requirements

Active TS/SCI with Full Scope Polygraph.
Professional or academic experience tuning neural networks, such as LLMs, on custom datasets.
Professional or academic experience developing AI/ML models and ensembles, selecting appropriate Python libraries and preprocessing actions, analyzing data, and assessing model performance.
Professional or academic experience using Python or R.
Professional or academic experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras.
Professional or academic experience and proficiency with SQL, including common table expressions, set operations, aggregate functions, and nested subqueries.
Professional or academic experience with version control systems such as GitHub and Jenkins.
Professional or academic experience leveraging GPUs for accelerated computing.
Professional or academic experience testing, evaluating, or validating AI systems, especially LLM-based systems.
Professional or academic experience designing and executing experiments to test LLM performance against a benchmark.
RAG testing experience.

Nice To Haves

Professional or academic experience with the Hugging Face Transformers library and hub.
Professional or academic experience developing benchmarks to measure LLM performance.
Cloud computing development and architecture experience.
Experience working with front-end web development frameworks such as Flask.
Experience creating machine learning models for text classification and topic modeling in Python using standard machine learning (scikit-learn) or deep learning models.
Experience developing applications for semantic search.
Professional or academic experience and proficiency with Tableau for producing visualizations and dashboards.
Academic or professional experience communicating methodological choices and model results.
Experience working with verification and validation test benches.
Experience working with Explainable AI (XAI) techniques.
Experience working with ONNX (Open Neural Network Exchange).

Responsibilities

Tune neural networks, including LLMs, on custom datasets and apply the results to specific use cases.
Develop AI/ML models and ensembles, select appropriate Python libraries and preprocessing methods, analyze data, and assess model performance.
Use Python or R for machine learning work.
Use deep learning frameworks such as PyTorch, TensorFlow, or Keras.
Use SQL, including common table expressions, set operations, aggregate functions, and nested subqueries.
Use version control systems such as GitHub and Jenkins.
Leverage GPUs for accelerated computing.
Test, evaluate, or validate AI systems, with a focus on LLM-based systems.
Design and execute experiments to benchmark LLM performance.
Conduct RAG testing.