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 US federal government clients in defense and national security. You'll build MLOps frameworks, optimize production models, and create custom algorithms that deliver mission-critical value.
What you'll do
- Develop MLOps frameworks and workflows across multiple domains and applications
- Deploy, maintain, and optimize ML models and data processes in production environments
- Design custom AI/ML algorithms aligned with mission objectives
- Conduct experiments and fine-tune algorithms to improve accuracy and efficiency
- Collaborate with cross-functional teams to integrate AI/ML models into products and services
What they're looking for
- Machine learning and deep learning
- MLOps and model deployment
- Algorithm development and optimization
- Python or similar programming languages
- Production environment management
- Experiment design and evaluation
- Cross-functional collaboration
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Professional certifications and industry training
- Hands-on learning and development opportunities
- Collaborative and inclusive work environment
- Impact on national security and government missions
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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 MLOps frameworks. What tools and technologies have you used, and how did you handle model versioning and monitoring in production?
- Tell us about a time you optimized an ML model for production. What metrics did you focus on, and how did you balance accuracy with computational efficiency?