AI/ML Engineer

Accenture Federal Services•Washington, DC

6d•$108,400 - $203,400

About The Position

At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve people's lives. Our team of over 13,000 professionals is dedicated to leveraging technology and innovation for clients across defense, national security, public safety, civilian, and military health organizations. As part of Accenture Federal Services, a technology company within the global Accenture network, we offer a collaborative and supportive community. We are recognized as a Glassdoor Top 100 Best Place to Work, providing opportunities for growth, learning, and career advancement through hands-on experience, certifications, and industry training. Join us to drive meaningful and lasting change that advances missions and propels the government forward.

Requirements

Hands-on experience building, training, deploying, and maintaining machine learning models in production environments.
Strong proficiency in Python and experience with one or more machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost or Hugging Face.
Experience developing and maintaining end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
Experience with MLOps practices and tools, including model versioning, CI/CD pipelines, model registries, feature stores, model monitoring, and automated retraining workflows.
Experience deploying machine learning models using cloud native or containerized technologies such as Docker, Kubernetes, Amazon SageMaker, Google Vertex, AI or Azure Machine Learning.
Experience monitoring production machine learning systems, troubleshooting model performance issues, and addressing model drift.
Must be a U.S. Citizen (No Dual citizenship).

Nice To Haves

Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required.

Responsibilities

Develop MLOps frameworks and workflows for a variety of domains and applications.
Build, train, deploy, and maintain machine learning models in production environments.
Design, develop, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, deployment, and monitoring.
Implement MLOps frameworks and best practices, including CI/CD pipelines, model versioning, model registries, feature stores, and automated retraining workflows.
Deploy, monitor, and optimize machine learning solutions using cloud platforms and containerized technologies such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML.
Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications while monitoring performance and addressing model drift.