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Arlington, VA
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Job Description
Role: AI Engineer (Agentic Workflows / AI Workbench)
Location: Washington, DC (Onsite / Hybrid)
Duration: Long-term contract
Job Description:
The AI Engineer implements and operationalizes agentic AI solutions using AI Workbench. This role builds intelligent agent workflows, integrates enterprise tools and data sources, implements RAG pipelines, and partners with DevOps/DataOps and QA to deliver reliable, secure, observable AI capabilities.
Work Conditions: Works with Architecture, DevOps/DataOps, QA, and Product roles to deliver end-to-end solutions.
Key Responsibilities:
• Develop and configure AI agents: goals, tools, policies, conversation flows, and lifecycle management within AI Workbench.
• Implement RAG pipelines: ingestion, preprocessing, chunking, embeddings generation, indexing, retrieval logic, and grounding.
• Integrate agents with enterprise systems via APIs/connectors and implement safe tool execution patterns.
• Collaborate with Solution Architect on design choices for security, scalability, observability, and cost optimization.
• Implement evaluation approaches: regression tests for prompts, accuracy checks for retrieval, and monitoring for drift and hallucination risk.
• Contribute to CI/CD pipelines for agent configurations, prompt libraries, and deployment automation.
• Document solutions, runbooks, and operational considerations to support hypercare and ongoing maintenance.
• Troubleshoot production issues and tune performance (latency, throughput, retrieval quality).
Required Qualifications:
• 4+ years of software engineering experience, including Python.
• Experience with API development/integration and microservices.
• Familiarity with containerized environments and basic cloud concepts.
• Strong problem-solving skills and ability to work in Agile teams.
Preferred Qualifications:
• Hands-on experience with GenAI/LLMs, prompt engineering, and RAG.
• Experience with vector databases (e.g., pgvector, Pinecone-like concepts, FAISS) and embeddings.
• Understanding of model evaluation, safety guardrails, and responsible AI practices.
• Experience with orchestration/agent frameworks and tool calling patterns.
Key Skills:
• Python, REST APIs, integration patterns
• RAG, embeddings, vector search concepts
• Prompt engineering and prompt versioning
• Testing/evaluation for AI behavior
• Observability and performance tuning
Location: Washington, DC (Onsite / Hybrid)
Duration: Long-term contract
Job Description:
The AI Engineer implements and operationalizes agentic AI solutions using AI Workbench. This role builds intelligent agent workflows, integrates enterprise tools and data sources, implements RAG pipelines, and partners with DevOps/DataOps and QA to deliver reliable, secure, observable AI capabilities.
Work Conditions: Works with Architecture, DevOps/DataOps, QA, and Product roles to deliver end-to-end solutions.
Key Responsibilities:
• Develop and configure AI agents: goals, tools, policies, conversation flows, and lifecycle management within AI Workbench.
• Implement RAG pipelines: ingestion, preprocessing, chunking, embeddings generation, indexing, retrieval logic, and grounding.
• Integrate agents with enterprise systems via APIs/connectors and implement safe tool execution patterns.
• Collaborate with Solution Architect on design choices for security, scalability, observability, and cost optimization.
• Implement evaluation approaches: regression tests for prompts, accuracy checks for retrieval, and monitoring for drift and hallucination risk.
• Contribute to CI/CD pipelines for agent configurations, prompt libraries, and deployment automation.
• Document solutions, runbooks, and operational considerations to support hypercare and ongoing maintenance.
• Troubleshoot production issues and tune performance (latency, throughput, retrieval quality).
Required Qualifications:
• 4+ years of software engineering experience, including Python.
• Experience with API development/integration and microservices.
• Familiarity with containerized environments and basic cloud concepts.
• Strong problem-solving skills and ability to work in Agile teams.
Preferred Qualifications:
• Hands-on experience with GenAI/LLMs, prompt engineering, and RAG.
• Experience with vector databases (e.g., pgvector, Pinecone-like concepts, FAISS) and embeddings.
• Understanding of model evaluation, safety guardrails, and responsible AI practices.
• Experience with orchestration/agent frameworks and tool calling patterns.
Key Skills:
• Python, REST APIs, integration patterns
• RAG, embeddings, vector search concepts
• Prompt engineering and prompt versioning
• Testing/evaluation for AI behavior
• Observability and performance tuning
group id: 91027337