DCX Agentic AI Engineer

Job Summary

Alation is pioneering the next era of data intelligence - powered by AI agents and trusted data. Organizations around the world rely on Alation to drive self-service analytics, cloud transformation, data governance, and AI innovation. Our technology helps enterprises connect people and data to make faster, smarter, and more confident decisions. With more than $340M in funding - valued at over billion and more than 650 customers, including 40% of the Fortune 100.

As data becomes the foundation of every business, Alation is leading the way in helping organizations unlock their full potential. By bringing intelligence, automation, and trust to every interaction, we empower customers to scale data-driven innovation with confidence and speed.

Joining Alation means being part of a winning team shaping the future of AI-powered data intelligence. We move fast, think big, and deliver results together.

As the Agentic AI Engineer for Content 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.

Core Responsibilities

• 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 skillset 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 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 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

What You Bring

• 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)