DCX Agentic AI Engineer

As the Agentic AI Engineer for Content and Enablement Systems within Digital Customer Experience, you'll spearhead the engineering behind Alation's next-generation enablement infrastructure. This is a high-impact, hands-on role where you'll own the build—designing the systems that synchronize, structure, and personalize content at scale. You'll thrive in a collaborative environment that values your autonomy: while you lead end-to-end development, you'll partner closely with enablement team leads and technical partners. This partnership model ensures you have a supportive framework for tackling complex challenges and the mentorship to accelerate your professional growth, offering you true ownership over meaningful technical systems with a built-in foundation for success.

The core responsibilities for the job include the following:

Agentic system building and evaluation:

• Design and ship agentic workflows end-to-end, not just prompting, but the surrounding tooling: orchestration, retries, guardrails, and evaluation harnesses.
• Apply the same core skill set across genuinely different problems, drift detection, content pipelines, and personalization, rather than specializing narrowly in one.
• Build evaluation loops that catch failures before they reach production, not just after.

Search indexing, retrieval, and API/MCP integration:

• 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.

Editorial judgment and enablement ops:

• 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.

Requirements:

• 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.

A big plus:

• 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).