Accenture Federal Services
AI / ML Engineer
About this role
Accenture Federal Services seeks an AI/ML Engineer to design and deploy LLM and RAG-based systems for US federal government clients. You'll combine GenAI expertise with core software engineering to build production-grade intelligent applications across defense, national security, and civilian missions.
What you'll do
- Design and maintain RAG pipelines including document ingestion, embeddings, vector storage, and LLM orchestration
- Build and optimize LLM applications for classification, summarization, Q&A, and workflow automation
- Apply software engineering and ML fundamentals to ensure performance, reliability, and security
- Implement and tune traditional ML models including regression, clustering, and classical NLP
- Integrate cloud services (Azure/AWS), data pipelines, and containerized workloads
- Collaborate with data engineers, architects, and mission subject matter experts on scalable solutions
What they're looking for
- Large Language Models (LLMs) and prompt engineering
- Retrieval-Augmented Generation (RAG) frameworks
- Python programming
- ML/DL frameworks (PyTorch, TensorFlow, HuggingFace)
- Cloud platforms (AWS or Azure)
- Docker and containerization
- Vector databases and embeddings
- Algorithms and data structures
Benefits
- Glassdoor Top 100 Best Place to Work recognition
- Collaborative and inclusive community environment
- Hands-on experience and industry training opportunities
- Certifications and professional development support
- Comprehensive benefits package (details available on career site)
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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
- Walk us through a RAG system you've built—how did you handle document ingestion, retrieval, and LLM integration?
- Describe your experience with prompt engineering and how you optimized LLM outputs for a specific application.