Ml Engineer Jobs

About The Position

Requirements

• Minimum of 5 years of experience in relevant fields including IT security, data science, software engineering, design, development, or deployment of AI or machine learning systems in enterprise or Government environments
• Experience working on data science, machine learning, or AI projects, either through internships, research projects, or professional roles.
• Experience building, training, and deploying machine learning models and AI systems in a production environment.
• Experience collaborating with cross-functional teams, including data scientists, software developers, and domain experts.
• Experience in data collection, cleaning, and analysis techniques to prepare datasets for AI modeling.
• Experience applying AI solutions to real-world problems in various enterprises and domains such as large corporations and Government agencies similar in size/scope to GSA, IRS, DoD or VA

Responsibilities

• Design, develop, train, test, deploy, and maintain AI and machine learning models for enterprise-scale and Government applications.
• Develop and operationalize production of AI/ML solutions, ensuring models and supporting systems are reliable, scalable, maintainable, and appropriate for VA mission requirements.
• Collect, clean, transform, analyze, and prepare structured and unstructured data for AI/ML model development, training, testing, and evaluation.
• Evaluate potential AI technologies, models, tools, frameworks, and architectures and recommend solutions appropriate for complex Federal Government and cybersecurity use cases.
• Apply AI and machine learning techniques to solve real-world business, cybersecurity, engineering, analytics, and operational problems across a large enterprise environment.
• Develop and support Generative AI and Large Language Model (LLM) solutions, including model configuration, system prompts, classifiers, fine-tuning, content moderation, safety filters, and other model controls.
• Evaluate AI-generated outputs for accuracy, factuality, grounding, reliability, bias, helpfulness, honesty, and overall model performance before outputs are incorporated into VA efforts.
• Design and execute AI/ML model evaluation and validation methodologies, including benchmark testing and comparative evaluation of model outputs.

Benefits

• Flexible work from home options available.