Accenture Federal Services
GenAI Engineer
About this role
Accenture Federal Services seeks a Generative AI Engineer to design and deploy cutting-edge AI solutions for federal government clients in defense, national security, and public safety. You'll develop custom GenAI applications, work with large language models, and collaborate with cross-functional teams to transform mission-critical operations.
What you'll do
- Design and build generative AI use cases tailored to client needs and specific data
- Evaluate and select appropriate large language models, including fine-tuning and deployment
- Develop agentic AI frameworks and determine when they're suitable for client problems
- Implement retrieval-augmented generation (RAG) and prompt engineering solutions
- Deploy GenAI models using cloud platforms such as AWS Bedrock, Google Vertex AI, and Amazon Kendra
- Collaborate with software engineers, data scientists, and solutions architects on innovative solutions
What they're looking for
- Generative AI and large language model expertise
- Prompt engineering
- Retrieval-augmented generation (RAG)
- Cloud platforms (AWS, Google Cloud, Azure)
- AI model selection and fine-tuning
- Agentic AI frameworks
- Rapid prototyping
- Problem-solving and technical acumen
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Collaborative and inclusive community
- Hands-on learning and growth opportunities
- Industry certifications and training programs
- Work on mission-critical federal government projects
- Opportunity to drive positive, lasting change
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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
- Can you walk us through your experience with Retrieval-Augmented Generation (RAG) and how you've implemented it in a production environment?
- Tell us about your experience with Large Language Models—how have you evaluated, fine-tuned, or deployed LLMs for a specific use case?