What we own: The end-to-end skill engine and governance ecosystem, spanning skill knowledge extraction, promotion pipelines, evaluation frameworks, self-correcting drift detection, and developer authoring environments.
How we build: Automated testing harnesses and release-gating pipelines running at enterprise scale, ensuring deterministic execution, strict schema compliance, and zero regressions for skills executing in live customer tenants.
Agentic engineering: We build evaluation tooling, synthetic datasets, and automated red-teaming pipelines that help engineers test how AI agents and Model Context Protocol (MCP) skills perform across complex tenant environments.
How we work: Close end-to-end collaboration across domain experts, AI platform engineers, and site reliability teams to transform domain knowledge into trustworthy, production-ready AI skills.
About the Role
We are seeking a Senior AI Evaluation Engineer (CI/CD & Platform) to own the continuous evaluation architecture, deployment pipeline infrastructure, and release gating for our enterprise AI Skills platform. In this role, you will build both the MLOps/CI-CD deployment pipelines and the automated eval harnesses that ensure all production AI skills execute accurately, remain schema-compliant, and run safely in live customer environments.
You will bridge the gap between AI skill development and production platform engineering—leveraging modern evaluation frameworks (e.g., LangSmith, Braintrust, DeepEval) alongside CI/CD pipelines to evaluate agent trajectories, test schema contracts, and certify every skill before deployment.
Key Responsibilities
Continuous Evaluation:. Build tiered evaluation pipelines—separating instant static/schema checks for PR developer loops from comprehensive synthetic, multi-turn trajectory, and LLM-as-a-Judge eval suites for release promotion gates.
CI/CD Quality Gates & MLOps Infrastructure: Architect and maintain CI/CD build pipelines (GitHub Actions, GitLab CI), skill registry versioning, and runner environments to enforce automated "pass/fail" release gates on every pull request.
Deterministic Code & Schema Validation:. Develop synthetic data generation pipelines and automated red-teaming utilities that allow skill authors to instantly generate edge-case evaluation suites from raw API schemas and domain specifications.
Benchmark Datasets & Regression Testing: Curate versioned golden datasets and execute continuous regression testing across base model upgrades, prompt changes, and security boundary checks.
Production Observability: Ingest real-world production traces and failure logs back into benchmark datasets to continuously close test coverage gaps.
About You
Basic Qualifications
4+ years of professional software engineering experience in Python building production-grade systems—with hands-on strength in EITHER CI/CD pipeline engineering OR AI evaluation methodology, plus working familiarity with the other.
2+ years designing, building, and operating test automation frameworks (pytest), CI/CD build pipelines (GitHub Actions, GitLab CI), or automated release gates.
1+ years of hands-on experience evaluating or building Generative AI applications (LLM tool-calling, agent workflows, or structured output parsing).
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Other Qualifications
AI Evaluation Tooling: Direct experience with modern evaluation and observability frameworks (e.g., LangSmith, Braintrust, DeepEval, Promptfoo, Phoenix).
Schema Contracts & API Validation: Strong knowledge of schema validation libraries (JSON Schema, Pydantic, OpenAPI) and API contract testing.
Agentic Standards & MCP: Familiarity with the Model Context Protocol (MCP), tool-calling specifications, or agent execution harnesses.
Static Code Analysis: Experience using schema validation frameworks (Pydantic, JSON Schema), linters, and test automation tooling to enforce agreed code contracts.
AI Security & Governance: Understanding of core LLM failure modes (hallucinations, prompt injection, PII/PHI sanitization, RBAC boundaries).
Quality & Testing Discipline: Strong engineering rigor around test coverage, dataset versioning, evaluator calibration metrics, and regression prevention in continuous deployment pipelines.
Technical Communication & Leadership: Demonstrated ability to own problem domains end-to-end, collaborate closely with platform and product teams, and write clear technical documentation and architecture designs.
Workday Pay Transparency Statement (For EU Locations Only)
Listed below is the base salary range applicable to this position. Workday pay ranges (and the precise pay offered to the successful candidate) are based on a number of objective criteria such as relevant experience and skills, and educational qualifications, level of responsibility, demands of the role, work location and business need. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants awarded by Workday Inc. For more information regarding Workday’s comprehensive benefits, please click here.
Primary Location Base Pay Range: €96,000 EUR - €144,000 EUR Ireland
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.
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