Production ML Engineer | Federal-Scale MLOps

Accenture Federal Services; Washington (District of Columbia); On-site; USD 108,000 - 203,000; Full time

Job summary

Accenture Federal Services seeks a machine learning engineer to design, build, and deploy production ML pipelines and MLOps frameworks across US federal domains. You will implement end-to-end ML workflows, monitor model performance, and address drift while collaborating with cross-functional teams.

You will work with cloud-native tools such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML, and contribute to continuous delivery of mission-critical AI solutions for defense, public safety,

Qualifications

• Hands-on experience building, training, deploying, and maintaining ML models in production.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost, or Hugging Face).
• Experience developing end-to-end ML pipelines (data 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/containers (Docker, Kubernetes, SageMaker, Vertex AI, Azure ML).
• Experience monitoring production ML systems, troubleshooting performance, addressing drift.

Responsibilities

• Develop MLOps frameworks and workflows for diverse domains and applications.
• Build, train, deploy, and maintain ML models in production environments.
• Design, develop, and maintain end-to-end ML pipelines (data ingestion, feature engineering, training, validation, deployment, monitoring).
• Implement MLOps best practices: CI/CD pipelines, model versioning, registries, feature stores, retraining workflows; deploy and monitor using cloud or container tech.
• Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical apps while monitoring performance and addressing drift.

Skills

Python

ML frameworks

MLOps

Docker

Kubernetes

Cloud platforms

Model monitoring

CI/CD

Model deployment

Data ingestion

Feature engineering

Model training

Hyperparameter tuning

Education

Advanced degree (PhD preferred)

Tools

SageMaker

Vertex AI

Azure ML

TensorFlow

PyTorch

Scikit-learn

XGBoost

Hugging Face

Job description

Accenture Federal Services seeks a machine learning engineer to design, build, and deploy production ML pipelines and MLOps frameworks across US federal domains. You will implement end-to-end ML workflows, monitor model performance, and address drift while collaborating with cross-functional teams.

You will work with cloud-native tools such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML, and contribute to continuous delivery of mission-critical AI solutions for defense, public safety,