Machine Learning Engineer

At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations.
Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more.
Join us to drive positive, lasting change that moves missions and the government forward!
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. 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.
Confidentiality matters: We don’t share mission or customer specifics in public postings. If you advance, we’ll provide program‑level details as appropriate during the interview process.
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, 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.
Here’s what you need:
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)