• Review and validate AI-generated credit analysis outputs against underlying debt documentation • Identify inaccuracies, inconsistencies, missing context, or overlooked risks in AI-generated conclusions • Ensure analytical outputs are complete, accurate, and commercially relevant • Continuously improve the quality and reliability of AI-assisted review workflows • Analyze senior secured credit agreements, bond indentures, loan facilities, and related debt documentation • Interpret covenant structures, lender protections, borrower restrictions, and capital stack implications • Identify loopholes, liquidity concerns, covenant risks, and restructuring vulnerabilities • Synthesize complex legal-financial language into concise, actionable summaries • Review financial statements including 10-Ks, 10-Qs, balance sheets, income statements, cash flow statements, and P&Ls • Assess borrower financial health in relation to debt obligations and covenant compliance • Evaluate distressed companies across sectors such as healthcare, software, and technology • Analyze liquidity pressures, restructuring implications, and broader credit risk indicators • Support distressed company research and target sourcing initiatives using public filings and market information • Review SEC EDGAR filings and related documentation to support analytical workflows • Assist in building scalable research and analysis processes • Operate independently in a fast-paced startup environment with strong ownership and accountability • Prepare concise, high-signal summaries for founder and leadership review • Clearly communicate covenant concerns, documentation inconsistencies, restructuring risks, and AI validation findings • Surface issues proactively and support decision-making with structured analytical insights

• Participate in global AI architecture reviews and provide solution guidance across AI, data, and IT domains • Advise teams on AI solution design, platform selection, scalability, integration patterns, and enterprise alignment • Help mature AI initiatives from proof of concept to production-ready enterprise solutions • Develop and maintain AI frameworks, reference architectures, policies, standards, and governance guidelines • Translate AI regulatory, compliance, risk, and governance requirements into practical implementation guidance • Embed responsible AI, security, privacy, and governance practices into delivery processes and platforms • Create enablement materials including tutorials, templates, checklists, and best practice documentation • Promote consistency, reusability, and operational excellence across global AI initiatives • Collaborate with architects, engineering, security, privacy, compliance, and business teams across regions • Support knowledge sharing, internal AI communities, and enterprise AI adoption strategies

• Define model governance for AI/ML at Mercury. • Continuously build and enhance the frameworks for validation, monitoring, and governance. • Own validation, monitoring, and governance of Mercury’s AI/ML model portfolio. • Partner closely with data scientists, engineers, compliance leads, and product teams. • Shape Mercury’s approach to model risk management in the context of AI. • Perform independent validation across predictive ML models and generative AI systems. • Assess risks in LLM-powered applications and identify model limitations. • Serve as a trusted advisor throughout the AI/ML lifecycle. • Help shape responsible AI standards including explainability and bias assessment. • Develop AI-enabled automation tools and modernize the MRM function. • Champion MRM as a strategic enabler for AI/ML adoption across teams.

Role Description You will support the design and development of practical AI-enabled learning systems, workflows, and technical learning experiences that help learners understand and apply AI tools in real-world environments. The successful candidate will combine hands-on AI implementation experience with the ability to translate complex concepts into accessible and engaging learner experiences. This is a highly collaborative role requiring someone comfortable operating across experimentation, tooling, workflow design, learning systems, prototyping, QA, and implementation. Key Responsibilities - Design and support AI-enabled learning systems and technical learning experiences - Help shape AI Builder curriculum content, workflows, labs, and learner journeys - Create and review practical AI exercises, prompts, demos, and workflow examples - Prototype AI-enabled workflows, automations, copilots, or learning experiences - Support technical QA and testing across AI learning products and experiences - Translate complex AI concepts into practical learner-friendly experiences - Collaborate closely with instructional designers, production teams, and technical stakeholders - Evaluate emerging AI tools, workflows, and platforms for learning use cases - Support facilitator guidance, learner enablement materials, and technical documentation - Contribute to experimentation, iteration, and continuous improvement across AI learning products - Identify technical risks, usability challenges, and implementation considerations early - Support scalable and commercially relevant approaches to AI-enabled learning experiences Qualifications - Strong hands-on experience using modern AI tools, platforms, or workflows in practice - Experience building, testing, or implementing AI-enabled workflows, copilots, automations, assistants, or user experiences - Comfortable working in fast-moving, evolving technical environments - Ability to explain technical AI concepts clearly to non-technical audiences - Strong implementation, experimentation, and problem-solving mindset - Experience supporting technical learning, enablement, education, or product adoption initiatives - Excellent communication and stakeholder collaboration skills - Ability to bridge technical and non-technical conversations effectively Requirements - Experience within learning technology, developer education, technical training, or AI enablement environments - Exposure to OpenAI APIs, prompt engineering, workflow automation, or AI agent tooling - Experience creating labs, sandbox exercises, workshops, walkthroughs, or facilitator-led learning experiences - Experience collaborating with instructional design or learning production teams - Consulting, freelance, or transformation programme experience - Experience evaluating practical enterprise use cases for AI adoption Benefits - Flexible remote working environment - Opportunity to grow into greater project ownership - Exposure to multiple projects and delivery teams - Collaborative, fast-paced environment with strong development potential