DCX Agentic AI Engineer

Support retrieval infrastructure end-to-end — indexing strategy, relevance tuning, and diagnosing degradation in retrieval quality

Integrate and reason about MCP servers and APIs as a first-class part of system design, not a bolt-on

Build systems that make human review of AI-generated content efficient, recognizing that a quality gate is still needed

Partner with Product Management, Product Marketing, Support, and field teams as ongoing stakeholders — not just at requirements-gathering, but throughout the build

3–5 years of relevant software engineering experience, with hands-on work building agentic AI systems, RAG pipelines, or similar retrieval-augmented applications

A systems-first mindset for designing modular AI agents that can scale across disparate data sources without creating technical debt.

Experience mapping complex, end-to-end agentic workflows to anticipate and mitigate failure modes before they reach production.

Experience with search/retrieval infrastructure — indexing, ranking, or relevance tuning

Familiarity with API integration and/or MCP (Model Context Protocol) server integration in agent-based systems

Comfort working cross-functionally with non-engineering stakeholders (PM, PMM, Support, Sales) to translate business content needs into technical systems

Strong judgment on when AI output needs human review vs. when it can ship autonomously

Experience in a fast-moving product company environment (vs. purely research or consulting settings)

Prior experience with content/documentation pipelines, developer enablement tooling, or knowledge-management systems

Experience with drift-detection or sync systems between code and documentation

Familiarity with evaluation frameworks/tooling for LLM-based agents specifically (not just traditional ML evaluation)

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