Data Wow Co., Ltd.

SDET

• Build evaluation suites for LLM and ML features, including curated test sets, reference-answer scoring, justified LLM-as-judge evaluation, and human review where appropriate.
• Define measurable quality standards with the delivery team before launch and hold releases to them.
• Test retrieval pipelines end to end, including chunking, indexing, retrieval quality, answer grounding, and missing-document behavior.
• Write regression tests for non-deterministic output using semantic similarity, structured-output validation, tolerance bands, and repeated sampling.
• Test client-relevant failure modes including hallucination, prompt injection, cross-tenant or cross-session data leakage, refusal and over-refusal, and Thai-language degradation.
• Track cost and latency alongside accuracy before release.
• Detect post-release drift by comparing production samples with the baseline and raise issues early.
• Design and maintain automated test suites for APIs, services, data pipelines, and web applications built for client projects.
• Run tests in CI and maintain fast, stable suites, treating flakiness as a defect.
• Set the test strategy at engagement start, including automation scope, manual testing, exit criteria, and client evidence.

Senior QA Automation Engineer

Artificial Intelligence

• Assess clients' current AI estates, including models in use, approvals, data access, and gaps between policy and practice
• Design AI governance operating models covering model inventories, risk classification, approval gates, human oversight, monitoring, and incident handling
• Lead readiness and gap assessments against ISO/IEC 42001, the EU AI Act, the NIST AI Risk Management Framework, and Thai regulatory expectations
• Develop sequenced remediation plans
• Conduct AI risk assessments for generative and LLM-based applications, including bias, fairness, explainability, data provenance, third-party dependencies, and failure modes
• Create AI policies, acceptable use standards, model documentation templates, risk registers, and committee terms of reference
• Prepare and support clients through certification audits and regulatory or enterprise-customer scrutiny
• Establish data governance foundations covering ownership, stewardship, classification, lineage, retention, and quality
• Connect governance requirements to data flows in client platforms, collaborating with data engineers
• Align governance design with PDPA and, where relevant, GDPR, in partnership with privacy and legal functions
• Own engagements end to end, including scope, planning, workshops, deliverables, and executive read-outs
• Build trust with senior stakeholders and provide principled advice
• Support commercial teams with scoping calls, proposals, pricing, and technical pitches
• Develop repeatable service offerings, assessment methods, and templates
• Represent Data Wow through client briefings, industry events, and written material on AI governance in Thailand
• Coach consultants and engineers on governance practices

Senior QA Automation Engineer

AI and Data Governance Consultant – Lead