Sr Associate Support Engineer, AI/ML and Platform Operations

About the role

Workday is looking for a customer-focused Support Engineer to drive incident resolution, root-cause analysis (RCA) and performance optimization across its enterprise platform and autonomous AI agent workflows. The role covers analyzing system metrics, debugging cloud-hosted ML service pipelines, inspecting LLM orchestration layers and managing critical customer escalations within strict SLAs, working closely with engineering and data science teams. It includes hands-on AI evaluation: reviewing LLM outputs, conversation logs and system traces to find failure modes and turn them into prompt, data and workflow improvements. The shift is Monday to Friday, 12:00pm to 9pm CT.

Key responsibilities

• Validate AI responses and troubleshoot functional processing errors in enterprise SaaS environments.
• Review LLM outputs, conversation logs and execution traces to find edge cases, hallucinations and failure modes; label data and report findings to senior engineers.
• Assist in root-cause analysis of software defects and pipeline failures using monitoring tools such as Kibana and Grafana, following diagnostic runbooks.
• Inspect enterprise AI workflows hosted on public clouds (AWS, GCP) and isolate breakdown points across model hosting services and API gateways.
• Monitor and triage support queues, enforce SLAs, handle high-severity incidents and take part in weekend on-call rotations.
• Communicate technical updates, workarounds and resolution steps to customer IT teams.
• Write SQL queries to check data integrity, inspect REST/SOAP API payloads (JSON/XML) and run Python or Bash scripts for diagnostics.
• Maintain investigation logs in Jira, ServiceNow or Salesforce, help keep runbooks current and flag recurring issues.

Required profile

• Minimum 1 year of experience in technical support, platform operations or application support for enterprise SaaS.
• Willingness and ability to cover scheduled weekends and holidays.
• Preferred: bachelor's degree in a technical field or equivalent practical experience, history of writing technical documentation, and strong problem-solving skills.

Required skills

• Writing and running intermediate SQL queries and diagnostic scripts.
• Reading API payloads (JSON/XML) and using basic Linux or Windows command-line tools.
• Navigating and monitoring AWS, GCP or Azure, and using observability tools such as Grafana, Kibana or Datadog.
• Evaluating AI/LLM conversation logs, traces and prompts.
• Ticket triage and basic troubleshooting.