• Develop applications integrating artificial intelligence as a core part of the solutions. • Create and implement agents with knowledge bases using GenAI platforms. • Apply and integrate generative AI solutions to solve complex problems. • Design and orchestrate agentic development flows using tools such as LangChain and LLMs. • Document and share solutions, promoting continuous learning within the team. • Serve as a technical reference, providing mentorship in AI and development best practices.

• Build AI‑powered automations for core People workflows (recruiting, onboarding, offboarding, employee lifecycle, compliance) • Translate defined workflow specs or process maps into built, tested, and deployed automations • Build and deploy skills or agentic workflows in partnership with the AI COE, including work tied to PROG’s active PIPER+ program • Use AI tools (Claude, Microsoft Copilot, GitHub Copilot, or similar) as core building tools — for workflow logic, automation, and agent design • Integrate automations with Workday, Jira, Microsoft 365, and other tools in the PROG stack (API and MCP support provided) • Ship quickly and iterate: deliver functional MVPs, gather feedback, and improve over time • Maintain and enhance automations as People needs evolve • Document what you build so others can understand, maintain, and extend it • Partner closely with TA, People Ops, Total Rewards, and HR Technology leaders

• Own the workforce enablement approach for AI‑driven workflow and role changes • Design and run enablement programs that drive real adoption — not “check‑the‑box” training • Equip managers to lead teams through transition, including readiness, support, and tough conversations • Translate redesigned workflows into clear role expectations, skills, and enablement plans • Build practical toolkits, learning experiences, and manager resources that actually get used • Partner across Talent, HR, and AI teams to ensure enablement comes before accountability shifts • Track adoption using leading indicators (behavior change, readiness, friction), not vanity metrics • Continuously refine approaches based on real signals from employees and managers

Note from FlexJobs Staff: The employer states this position is available anywhere in the world. AI Quality Control Specialist - Spanish Language About the Role: TrustScale is building scalable, high-quality human-in-the-loop systems to power AI. We are looking for an AI Quality Control Specialist - Spanish Language Specialist who can analyze, validate, and challenge AI-generated outputs with precision and consistency. This is not a proofreading role. You will act as a quality gatekeeper, ensuring outputs meet standards for accuracy, utility, and linguistic quality across large-scale workflows. What You Will Do: - Evaluate AI-generated content in Spanish across multiple task types (annotation, review, validation). - Apply structured quality frameworks to assess: - Accuracy and factual correctness. - Utility (alignment with user intent). - Language quality (fluency, tone, clarity). - Train vendors - Participate Clients Calls Identify and flag: - Hallucinations and misinformation. - Logical inconsistencies. - Cultural or linguistic mismatches. - Provide clear, structured feedback to improve upstream quality. - Detect patterns of errors across batches and contribute to quality insights. - Support calibration efforts to ensure consistent scoring across teams. - Contribute to guideline refinement and evaluation standards. Who You Are: - Professional proficiency in Spanish and strong command of English are required. - Highly analytical with strong critical thinking skills. - Comfortable working with ambiguous and evolving guidelines. - Detail-oriented with the ability to maintain consistency at scale. - Clear communicator, able to justify decisions and provide actionable feedback. - Naturally skepticalable to question outputs and validate information. Preferred Background: - Journalism, editorial, or investigative research. - Linguistics, translation, or localization QA. - Content moderation / Trust & Safety. - Experience in AI data annotation, evaluation, or QA. What Success Looks Like: - High consistency in quality scoring across tasks. - Strong alignment with QA benchmarks. - Ability to detect non-obvious quality issues. - Feedback that improves annotator performance and overall output quality. - Contribution to a scalable, predictable quality system. Why TrustScale: At TrustScale, we operate AI talent as a supply chainstructured, measurable, and optimized. You will be part of a system where quality is not subjective, but defined, measured, and continuously improved.