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Your work days are brighter here.
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.
About the Team
At Workday, we bring technical rigor, customer empathy, and a spirit of fun to enterprise software. Our support team underpins operational excellence across Workday's digital experience, AI/ML platform, and Agent Factory initiative. Workday’s Agent Factory is our internal engine that builds, trains, and deploys autonomous AI agents to execute complex HR and Finance workflows, including expense processing, hiring, and workforce scheduling. We partner with core engineering to eliminate bottlenecks, maintain high platform availability, and ensure system reliability for our global customer base.About the Role
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.
About You
Work Experience: Minimum 1 year of experience in technical support, platform operations, or application support for enterprise SaaS environments.
Technical & Diagnostic Skills:
Minimum 1 year of experience writing and executing intermediate SQL queries and running diagnostic scripts.
Minimum 1 year of experience inspecting API payloads (JSON or XML data structures) and utilizing basic Linux or Windows command-line tools.
Cloud & Monitoring Systems:
Minimum 1 year of hands-on experience navigating or monitoring services on public cloud platforms (AWS, GCP, or Azure).
Minimum 1 year of experience utilizing observability tools (e.g., Grafana, Kibana, Datadog) to view logs or trace outputs.
AI & LLM Exposure:
Minimum 1 year of experience inspecting, reading, or evaluating AI/LLM conversation logs, traces, or prompt outputs.
Triage & Operations:
Minimum 1 year of experience performing initial queue triage and basic troubleshooting using ticketing workflows.
Willingness and ability to work scheduled weekend and holiday coverage rotations.
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field (or equivalent practical work experience).
Experience following established runbooks to perform guided, autonomous technical troubleshooting.
History of drafting clear written technical documentation, post-incident updates, or resolution notes for customer-facing teams.
Strong active problem-solving skills and an adaptability to learn complex, multi-tiered enterprise systems.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!