Machine Learning Engineer
Accenturefederalservices · Washington, DC
About this role
**Machine Learning Engineer** At Accenture Federal Services (AFS), we use machine learning to solve high-impact, mission-critical problems across defense, national security, public safety, and federal civilian sectors. You’ll design, build, and deploy models that operate at federal scale—improving outcomes for millions of people. We ship in weeks, not quarters, and measure success through model performance, reliability, safety, and cost efficiency. **The work:** - 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 (data ingestion, feature engineering, training, validation, deployment, and monitoring) - Implement MLOps best practices (CI/CD pipelines, model versioning, model registries, feature stores, automated retraining) - Deploy, monitor, and optimize ML solutions using cloud platforms and containerized technologies (e.g., Docker, Kubernetes, SageMaker, Vertex AI, Azure ML) - Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications while monitoring performance and addressing model drift **Here’s what you need:** - Hands-on experience building, training, deploying, and maintaining ML models in production - Strong Python skills and experience with one or more ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face) - Experience developing and maintaining end-to-end ML pipelines (ingestion, feature engineering, training, validation, deployment, monitoring) - Experience with MLOps practices and tools (model versioning, CI/CD, model registries, feature stores, monitoring, automated retraining) - Experience deploying ML models using cloud-native or containerized technologies (Docker, Kubernetes, Amazon SageMaker, Google Vertex AI, Azure Machine Learning) - Experience monitoring production ML systems, troubleshooting performance issues, and addressing model drift
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