Sr Associate Support Engineer (AI/ML & Platform Operations)

We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too.

We are seeking a customer-focused Support Engineer to drive incident resolution, root-cause analysis (RCA), and performance optimisation across Workday’s enterprise platform and autonomous AI agent workflows. In this high-visibility role, you will analyze system metrics, debug cloud-hosted ML service pipelines, inspect LLM orchestration layers, and manage critical customer escalations within strict SLAs. You will also partner directly with engineering and data science teams through feature iteration and optimisation. A key part of this role involves hands-on AI evaluation: analysing LLM outputs, reviewing conversation logs, and digging into system traces to spot failure modes and translate those insights into prompt, data, and workflow improvements.

Key Responsibilities

Enterprise SaaS & Functional Domain Expertise: Apply foundational knowledge of enterprise applications to validate AI responses and assist in troubleshooting functional processing errors.

Hands-On AI Evaluation: Review LLM outputs, conversation logs, and execution traces to identify edge cases, hallucinations, and routine failure modes. Perform structured data labeling and report findings to senior engineering staff.

Technical Troubleshooting & RCA: Assist in root-cause analysis for software defects and pipeline execution failures using monitoring tools like Kibana and Grafana, following established diagnostic runbooks.

Cloud & LLM Diagnostics: Inspect enterprise AI workflows hosted in public cloud environments (AWS, GCP), helping isolate breakdown points across model hosting services and API gateways.

Incident & Queue Management: Monitor and triage support queues, enforce SLAs, and process incoming tickets efficiently. Support high-severity incident responses and participate in weekend on-call rotations.

Customer Communication: Communicate clear technical updates, standard workarounds, and resolution steps to customer IT teams, working with senior support for high-stakes escalations.

Database & Code Diagnostics: Write standard SQL queries to validate backend data integrity, inspect REST/SOAP API payloads (JSON/XML), and execute existing Python or Bash scripts to run routine diagnostics.

Documentation & Team Collaboration: Maintain detailed investigation logs in Jira, ServiceNow, or Salesforce, help maintain team runbooks, and flag recurring issue trends to senior engineers and product teams.