Machine Learning Engineer

Accenture Federal Services • Washington, DC

2d • $108,400 - $203,400

About The Position

Accenture Federal Services is seeking a Machine Learning Engineer to develop and operate production-grade machine learning systems for federal missions. The role focuses on building and maintaining models and end-to-end ML pipelines, implementing MLOps practices, and deploying scalable solutions in cloud and containerized environments. Work spans mission-critical areas including defense, national security, public safety, and federal civilian sectors. Systems are expected to meet high standards for reliability, traceability, responsible use, safety, performance, and cost efficiency. The job requires U.S. citizenship without dual citizenship.

Requirements

• Hands-on experience building, training, deploying, and maintaining machine learning models in production environments.
• Strong proficiency in Python.
• 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 with no dual citizenship.

Nice To Haves

• An advanced degree in computer science or a technology, engineering, or mathematics (STEM)-related field; a Ph.D. is 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 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.
• Collaborate with engineering and data teams to integrate scalable ML solutions into mission-critical applications.
• Monitor production performance, troubleshoot model issues, and address model drift.

Benefits

• A wide variety of benefits is offered; specific benefits are not listed in the posting.
• Hands-on experience, certifications, industry training, and access to current tooling and training.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed