Accenture Federal Services
Cleared AI/ML Engineer
About this role
Accenture Federal Services seeks a cleared AI/ML Engineer to develop and deploy machine learning solutions for U.S. federal government missions. You'll build MLOps frameworks, optimize production models, and create custom algorithms that drive real-world impact across defense and national security sectors.
What you'll do
- Build MLOps frameworks and workflows for diverse applications and domains
- Deploy, maintain, and optimize ML models in production environments
- Develop custom AI/ML algorithms aligned with mission objectives
- Run experiments and fine-tune models to improve performance and accuracy
- Partner with cross-functional teams to integrate AI/ML into products and services
What they're looking for
- Machine learning development and optimization
- MLOps and model deployment
- AI/ML algorithm design
- Data processing and pipeline development
- Python or similar programming languages
- Model evaluation and experimentation
- Cross-functional collaboration
- Production environment management
Benefits
- Work on mission-critical projects for U.S. federal government
- Glassdoor Top 100 Best Place to Work recognition
- Professional development through certifications and training
- Collaborative and inclusive workplace culture
- Hands-on learning opportunities
- Growth and advancement potential
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Accenture Federal Services
Accenture Federal Services builds and maintains mission-critical technology solutions for the U.S. federal government, including cloud infrastructure, enterprise systems integrations, and cyber defense tools. The company is hiring DevOps engineers, full-stack developers, SAP specialists, and test engineers to support classified and unclassified federal government projects.
- Website
- accenturefederal.com
Likely interview questions
- Describe your experience developing and deploying ML models in production environments. What challenges did you face and how did you address them?
- Walk us through your experience with MLOps frameworks and workflows. Which tools and platforms have you used?