Machine Learning Engineer

About the Role

Accenture Federal Services (AFS) uses machine learning to solve high-impact, mission-critical problems across defense, national security, public safety, and federal civilian sectors. Our 13,000+ colleagues deliver production-grade AI systems where reliability, traceability, and responsible use are non-negotiable. As part of our ML Engineering community, you’ll design, build, and deploy models that operate at federal scale, improving outcomes for millions of people. You’ll grow through hands-on delivery, certifications, and access to the latest tooling and training. We ship in weeks, not quarters, and we measure success through model performance, reliability, safety, and cost efficiency.

Key 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.

Requirements & Tech Stack

• 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 (No Dual citizenship).

Qualifications

• Bonus points if you have: Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required.