Note: The job is a remote job and is open to candidates in USA. Accenture Federal Services is a technology company serving the US federal government across defense, national security, public safety, civilian, and military health organizations. The AI/ML Engineer will develop, deploy, and maintain production machine learning systems and end-to-end MLOps pipelines, collaborating with engineering and data teams to integrate scalable solutions and address model performance and drift.
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. 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
Skills
- 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)
- Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States
- Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required
Benefits
- Certifications
- Industry training
Company Overview