AI/ML Engineer

Accenture Federal Services•Arlington, TX

21d

About The Position

The AI/ML Engineer will design, build, and maintain AI/ML lifecycle core services and products. Create utilities and ensure seamless function of orchestration capabilities to support the AI/ML environment. Develop and implement infrastructure for training, validating, and deploying machine learning models, create reusable components and libraries to accelerate AI/ML development, and build model serving platforms for efficient inference. Implement automated machine learning pipelines, design systems for model monitoring and performance tracking, and develop tools for model explainability and interpretability. Optimize AI/ML algorithms for performance and scalability, implement MLOps practices for continuous integration and deployment of models, and collaborate with data engineers to ensure data pipelines support AI/ML requirements. Stay current with advances in AI/ML technologies, document AI/ML systems and processes, and provide technical guidance to data scientists implementing models. Troubleshoot complex issues in AI/ML systems, implement version control for models and datasets, and develop testing frameworks for AI/ML components.

Requirements

4 years of experience developing, deploying, and supporting AI/ML models in enterprise environments
Bachelor’s degree (or an additional 4 years of experience) in IT, Cybersecurity, Computer Science, Information Systems, Data Science, Software Engineering, or related field
Meet the DoD 8140 requirements
Active Secret, Top Secret, TS/SCI, or TS/SCI with Polygraph clearance required, depending on position

Responsibilities

Design, build, and maintain AI/ML lifecycle core services and products.
Create utilities and ensure seamless function of orchestration capabilities to support the AI/ML environment.
Develop and implement infrastructure for training, validating, and deploying machine learning models.
Create reusable components and libraries to accelerate AI/ML development.
Build model serving platforms for efficient inference.
Implement automated machine learning pipelines.
Design systems for model monitoring and performance tracking.
Develop tools for model explainability and interpretability.
Optimize AI/ML algorithms for performance and scalability.
Implement MLOps practices for continuous integration and deployment of models.
Collaborate with data engineers to ensure data pipelines support AI/ML requirements.
Stay current with advances in AI/ML technologies.
Document AI/ML systems and processes.
Provide technical guidance to data scientists implementing models.
Troubleshoot complex issues in AI/ML systems.
Implement version control for models and datasets.
Develop testing frameworks for AI/ML components.