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Develop, integrate, and maintain AI agent workflows, prompt chains, and tool-calling integrations for AgentQ. Build backend connectors and data retrieval pipelines while creating automated test suites to ensure agent accuracy and performance.
This is a remote position.
We are seeking two AI Engineers to support the development, integration, testing, and deployment of AgentQ workflows. Working closely with our Senior Agentic AI Engineers and backend teams, you will build agent tools, API connectors, prompt pipelines, and automated evaluation suites to bring reliable AI automation into production.
Key Responsibilities:
Develop, configure, and maintain AI agent workflows, prompt chains, and tool/function-calling integrations for AgentQ.
Build backend connectors, REST APIs, and lightweight data retrieval pipelines (RAG) linking enterprise data sources and vector stores with agent workflows.
Create and run automated test suites, benchmark datasets, and quality evaluations to measure agent accuracy, latency, and guardrail compliance.
Debug edge cases, monitor production agent runs, and iterate on prompts, retrieval parameters, and fallback logic.
Document agent architectures, API interfaces, and operational runbooks for cross-functional engineering teams.
2+ years of hands-on experience in Python software engineering or applied AI/LLM development.
Practical experience building LLM-powered applications, RAG pipelines, or agent workflows (e.g., LangChain, LlamaIndex, OpenAI/Anthropic/Mosaic AI APIs).
Solid backend engineering fundamentals: REST APIs (FastAPI/Flask), JSON schemas, asynchronous programming, and Git workflows.
Familiarity with vector databases, embeddings, and basic SQL/cloud data environments (Databricks, AWS, or Azure).
Strong problem-solving mindset and ability to iterate quickly based on evaluation metrics and user feedback.
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