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Costa Rica
Hybrid
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 optimization 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 optimization. A key part of this role involves hands-on AI evaluation: analyzing 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 operational knowledge of enterprise applications and workflows to validate AI logic and troubleshoot functional processing errors.
Hands-On AI Evaluation: Regularly review LLM outputs, AI conversation logs, and execution traces to identify edge cases, hallucinations, and failure modes. Perform data labeling and translate diagnostic insights into actionable updates for prompts, workflows, and system logic.
Technical Troubleshooting & RCA: Perform root-cause analysis on software defects, performance bottlenecks, and LLM agent execution failures using Kibana, Grafana, and other cloud telemetry tools.
Cloud & LLM Diagnostics: Debug enterprise AI workflows hosted across public cloud environments (AWS, GCP), isolating issues across model hosting services, API gateways, and LLM reasoning pipelines.
Incident & Queue Management: Triage high-severity (P1) support queues, enforce SLAs, and prioritize critical outages over routine inquiries. Participate in weekend on-call rotations for continuous coverage.
Customer Success: Act as the primary technical escalation point for customer IT leadership, clearly explaining root causes, workarounds, and resolution plans during critical incidents.
Database & Code Diagnostics: Write complex SQL queries to validate backend data integrity, debug REST/SOAP API payloads (JSON/XML), and use Python or Bash scripts to automate diagnostics.
Reliability & Product Partnership: Document detailed investigation traces in Jira, ServiceNow, or Salesforce, update runbooks, and partner with engineering and data science teams to deliver permanent fixes and address issue trends.
About You
Basic Qualifications (Required)
Work Experience: Minimum 3 years of experience in technical support engineering, platform operations, or escalation management for enterprise SaaS platforms.
Advanced AI & LLM Systems: Minimum 2 years of hands-on experience reviewing, analyzing, or troubleshooting Large Language Model (LLM) pipelines, prompt/tool-calling structures, or conversation traces/logs.
Cloud & Monitoring Diagnostics:
Minimum 2 years of hands-on experience monitoring, debugging, or troubleshooting services on public cloud infrastructure (AWS, GCP, or Azure).
Minimum 2 years of experience using enterprise monitoring and observability tooling (e.g., Grafana, Kibana, Datadog, or Prometheus).
Technical Stack & Coding:
Minimum 2 years of experience using programming languages or writing and executing SQL queries for data analysis and tuning.
Minimum 2 years of experience analyzing API structures and data serialization formats (JSON or XML).
Minimum 2 years of experience troubleshooting operating systems (Linux or Windows) and cloud networking components.
Operational Availability: Willingness and ability to participate in scheduled weekend on-call coverage rotations.
Preferred Qualifications
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent practical work experience).
Foundational exposure to Explainable AI (XAI) concepts and model interpretability framework analysis.
Demonstrated history of prioritizing support queues, managing severe incident escalations, and de-escalating critical customer issues.
Strong written and verbal communication skills with the ability to convey complex technical diagnoses to non-technical stakeholders and cross-functional partners.
Our Approach to Flexible Work
With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
Workday is committed to providing reasonable accommodations for qualified individuals during our application process, in order to perform one or more essential functions of their job, as well as regarding the use of AI tools for employment decision-making to any degree. Please see below for more details including how to request an accommodation as a qualified veteran, due to a disability or for religious reasons, or as otherwise provided under applicable law.
Workday prohibits taking adverse action against any candidate or employee for reporting a possible violation of this policy, requesting one or more work accommodations, exercising a privacy right, or cooperating in an investigation in accordance with applicable law. Any employee who retaliates against a candidate or employee for doing so may be subject to disciplinary action, up to and including termination of employment, to the fullest extent allowable under applicable law.
If you require a reasonable accommodation, you may email accommodations@workday.com, as far in advance as possible.
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