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# AI/ML Engineer

- **Company:** Accenture
- **Location:** Washington, DC, United States
- **Salary:** $108,400.0 - $203,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Feature Engineering, Data Ingestion, Pytorch, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Xgboost, Machine Learning Operations, Software Version Control, Docker
- **Published:** September 17, 2026
- **Apply:** https://www.dcjobsite.com/job.asp?id=3394241884&tx=JT9688TYV&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 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)

Bonus points if you have:

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

## Description

* 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. And the last one.
 * Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications while monitoring performance and addressing model drift.


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