Accenture Federal Services
AI / ML Engineer
About this role
Accenture Federal Services seeks an AI/ML Engineer to develop and integrate agentic AI systems with existing data platforms in Chantilly, VA. You'll work on retrieval pipelines, agent tools, data ingestion, and microservices to support federal government missions in defense and national security.
What you'll do
- Develop retrieval pipelines including metadata indexing, embeddings, and hybrid search capabilities
- Build and deploy agent tools for structured queries, semantic search, and workflow orchestration
- Create ingestion and indexing pipelines for data lakes and vector stores
- Implement APIs, microservices, and backend logic for agentic systems
- Optimize query routing, caching, and grounding strategies
- Support prototyping, demos, and pilot deployments
What they're looking for
- Python programming
- APIs and microservices development
- Cloud services
- DevOps tools and principles
- Vector databases
- RAG (Retrieval-Augmented Generation)
- Agent frameworks
- SQL and distributed systems
Benefits
- Collaborative and caring work environment
- Professional growth through certifications and industry training
- Hands-on learning opportunities
- Competitive compensation ($120,500–$221,800 in select states)
- Comprehensive benefits package
- Glassdoor Top 100 Best Place to Work recognition
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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 your experience building retrieval pipelines—specifically how you've implemented metadata indexing, embeddings, and hybrid search in past projects.
- Describe your experience integrating agentic AI systems or agent frameworks with existing data platforms. What challenges did you encounter and how did you solve them?