Mercury - AI Solutions Architect
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Requirements
• 1–3 years of experience in configuration of customer-facing AI agent/chatbot (e.g., Fin by Intercom, Ada, Zendesk AI) • 5+ years of experience in customer support backend operations or CS systems administration • Systems thinker and problem solver, with experience in testing new solutions and change management for CS teams • Strong technical acumen, with the ability to understand and communicate technical concepts to both technical and non-technical audiences • Analytical thinking: ability to interpret data quickly and translate it into actionable decisions • Cross-collaboration: works effectively with cross-functional partners across Engineering, Product, Compliance, Core Customer Support • Stakeholder communication: delivers clear, concise updates to stakeholders at all levels • Adaptability and a growth mindset, thriving in a fast-paced, ever-evolving environment • Proven ability to work cross-functionally, particularly with technical teams like Engineering, Product, and Security • An interest in software development or engineering, enabling deeper technical conversations with our engineering teams • Experience with core customer support platforms such as Zendesk, Guru, MaestroQA/Rippit • Proficiency in SQL and familiarity with navigating data tables • Experience supporting remote or distributed workforce models • The total rewards package at Mercury includes base salary, equity (stock options), and benefits. • Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers. • Our target new hire base salary ranges for this role are the following: • US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $143,400 - $168,700 USD • US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $129,100 - $151,800 USD • Canadian employees (any location): CAD $130,500 - $153,500
Responsibilities
• Strategy driver: Partner with CS and Product leadership to define the strategy and roadmap for our chatbot and email AI agents to contribute to company OKRs • Strategy driver: • Architect and optimize AI agent workflows and procedures: Design simple, reliable conversation workflows and automations so our chatbot asks the right questions, clarifies common customer needs, and responds with accurate information, appropriately escalating to live support when needed. • Architect and optimize AI agent workflows and procedures: • Quality reviews, analytics and bot coaching: Conduct regular reviews of bot conversations and high-level report analysis to identify trends, areas of opportunity and potential risk within the chatbot. • Quality reviews, analytics and bot coaching: • Cross-collaboration to improve resolution rate and quality: Work with partners in Engineering, Product and CS Strategy and Ops to maximize the types of interactions the chatbot can support and resolve - connecting new data sources or systems on the backend to give the bot greater scope, implementing new workflows that use this data to solve new customers requests without live support intervention. • Cross-collaboration to improve resolution rate and quality: • Optimize the AI Agent experience across all channels: Expand upon the types of interactions and experiences that qualify for the email AI agent and other asynchronous channels. • Optimize the AI Agent experience across all channels: • Compliance and security: Ensure Mercury’s high standards for security and compliance are woven into the foundations of our AI-assisted CS strategy • Compliance and security: • AI vendor relationship owner: Own the relationship with AI vendors for CS solutions, including Intercom. Raising issues, requesting fixes, staying on top of product releases and coordinating changes that impact CS operations • AI vendor relationship owner: • Scoping of internal agent co-pilot: Drive efforts to assess and implement agent assist AI tools to maximise agent efficiency • Scoping of internal agent co-pilot: • Evaluation of AI tooling for CS partners: Support other teams, such as Learning and Development and QA in evaluating how other tools’ AI offerings can increase efficiency across CS. • Evaluation of AI tooling for CS partners:
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