NeuraFlash, Part of Accenture
Salesforce Solution Engineer, RCA & RCB
Remote - Canada (Alberta, British Columbia, Ontario Only) · Senior · Full-time
Work authorization requiredDetected 20 days ago
AWSLLMsAgentic AISalesforceCRMPipeline ManagementForecastingSAPERPCustomer SuccessLeadershipCollaborationMentoringNetSuite
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- From conversational AI to predictive analytics, we empower organizations to stay ahead in an ever-evolving digital landscape with cutting-edge, tailored strategies.
- We know that a perfect 9-5 isn't possible.
Responsibilities
- Practice Leadership: Lead Revenue Cloud/Billing & AI Sales within the Solutions Engineering practice. You will develop strategies, mentor team members, and establish best-in-class pre-sales processes.
- Go-to-Market Execution: Drive go-to-market efforts by positioning the firm as a premier partner for modern Salesforce Revenue Cloud Advanced/Billing, Sales Cloud, and Agentforce autonomous AI deployments.
- Pre-Sales Excellence: Lead deep-dive pre-sales discovery sessions, gather complex billing/monetization requirements, and deliver compelling, value-driven demonstrations showcasing modern Revenue Cloud capabilities (including Salesforce CPQ, Subscription Management, and multi-attribute pricing engines).
- AI & Sales Transformation: Architect and demonstrate comprehensive deal cycles that transcend core CPQ, incorporating Sales Cloud and Agentforce Sales/SDR agents to showcase how autonomous AI can automate pipeline generation, qualify leads, and streamline the entire quoting lifecycle.
- Scoping & SOWs: Develop and deliver highly technical proposals, solution architectures, and Statements of Work (SOWs), ensuring scoping accuracy and market competitiveness.
- Cross-Functional Bridge: Act as the technical liaison between sales, delivery, and product teams-streamlining customer sales responses, product improvements, and solution designs.
- Product Innovation: Support product innovation by providing real-world field feedback to Salesforce product managers regarding Revenue Cloud features, Sales Cloud enhancements, and Agentforce behavior.
- Lead and Shape the Practice: Influence the strategy, execution, and customer success metrics of a high-growth Revenue Cloud and AI sales division.
- We build a culture of trust and understanding.
Requirements
- So you have to jump out to do any of those, no problem!
- United States (Required)
Nice to have
- 2+ years of Pre-Sales / Solutions Engineering experience, including leading discovery, designing complex architecture blueprints, and responding to technical RFPs/RFIs.
Compensation
- Enjoy a strong base salary, performance incentives, and a comprehensive enterprise benefits package.
Benefits
- Continuous Learning: Stay at the absolute forefront of Salesforce ecosystem innovations, product releases, and next-generation autonomous AI capabilities to drive creative solutions for our clients.
- Education: BS in Computer Science, Information Systems, Engineering, or a related technical/business field (or equivalent practical experience).
- Limitless Growth: Work alongside top-tier AI and Salesforce professionals in a dynamic culture that prioritizes continuous learning and career acceleration.
Company info
- Why NeuraFlash, Part of Accenture: At NeuraFlash, Part of Accenture, we are redefining the future of business through the power of AI and groundbreaking technologies like Agentforce.
- We are proud to be creating the future of generative AI and AI agents.
- Be part of the NeuraFlash, Part of Accenture journey and help shape the next wave of AI-powered transformation.
- Ability to quickly build bespoke demos to show customers the "Art of the Possible" within Revenue Cloud/Billing / AI Sales / B2B Commerce
Visa & Work Authorization
- Work Authorization: United States (Required)
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This listing is sourced directly from NeuraFlash, Part of Accenture's careers page and normalized into a canonical job model.