Operations Manager, R&D Operations & AI Enablement

About the role

Who is Whip Around? Whip Around is an award-winning, easy-to-use fleet maintenance software company that connects drivers, mechanics, and fleet managers to improve the uptime of vehicles and equipment. Founded in 2016 and headquartered in San Diego, CA, we’re on a mission to keep the world’s fleets moving. Team culture is at the heart of our success, with personal improvement being just as important as company outcomes. We boast a senior leadership team with experience from numerous successful global businesses, and we continue to double down on our vision to bring simplicity and efficiency to fleet management and compliance — increasingly powered by data and artificial intelligence. Why this role is important to us: Reporting to the Director of Operations, the Operations Manager helps improve our internal AI tools and agents while supporting our R&D and Engineering teams with operational measurement, analytics and reporting. Working closely with the Director of Operations and our technical teams, this role helps investigate, implement, validate and continuously improve internal AI solutions, while supporting the data, systems and processes that keep them accurate and reliable. A key part of the role is supporting Whip Around’s AI enablement efforts across customer-facing teams. Under the direction of the Director of Operations, the Operations Manager will help identify practical opportunities for AI and automation, turn promising experiments into reliable, repeatable solutions, and embed those tools into everyday workflows to save time and improve consistency. We’re looking for someone with hands-on experience helping teams put AI into practical use, improve the quality and reliability of AI-assisted workflows, and demonstrate measurable value from the solutions they implement. What success looks like: Operational Delivery & Support • Support the Director of Operations in keeping workflows, resources and projects on track and aligned to team goals and developing initiatives. • Track and report on operational KPIs against targets set with the Director, flagging performance gaps and helping put corrective action in place. • Help manage operational budgets, systems and vendors to ensure we receive appropriate value from our investments. • Identify new opportunities for AI within assigned areas of responsibility and help design and implement practical solutions.. AI Enablement & Use-Case Delivery • Support the rollout and adoption of AI and automation tools across the team, providing guides, training and hands-on support people need to use them confidently and effectively. • Help identify and prioritize practical AI use cases tied to clear outcomes — reducing manual effort, improving consistency and saving time or cost. • Deliver early, practical “quick wins” in the first few months, then help establish repeatable processes for monitoring evaluating and improving solutions already in use.. • Help ensure live AI tools and agents have access to accurate, current and appropriate information so they remain reliable and trusted by the people using them. • Maintain clear communication with stakeholders across the business to ensure AI agents and workflows remain aligned with current data, processes and organizational changes. Process Improvement & Systems • Map and improve core operational processes, identifying bottlenecks where automation or AI could reduce manual administration and sharing recommendations with the Director. • Support R&D and Engineering AI initiatives and help teams improve reporting and data quality. • Document processes, tools and standard operating procedures so that knowledge is captured, repeatable and easy for the team to follow. • Manage and monitor new system integrations and coordinate new AI agent requests and intake as they arise. Data, Reporting & Stakeholder Support • Help develop and maintain meaningful R&D and Engineering metrics and reporting, using data from development and work-management systems to provide visibility into delivery, capacity, workflow health and operational trends. • Build strong relationships with R&D and engineering to develop a deep understanding of their of their workflows, needs and challenges, and help design AI-assisted reporting that provides useful operational insights while supporting broader company reporting requirements. • Conduct regular audits of key business data and operational data to ensure accuracy , working with relevant teams to identify and resolve sources of variance. • Help prepare monthly and quarterly analytics for the Director and senior leadership, communicating findings clearly to both technical and non-technical audiences. Collaboration & Team Support • Work closely with the Director of Operations and partner with other Operations Managers to keep work coordinated across teams. • Bring structure, timelines and follow-through to operational projects so they stay on track. • Provide documentation, support and training for internal users. Compliance & Quality • Follow and help maintain responsible-use, data privacy and quality controls for AI and automation, so the tools we use stay safe and trustworthy. • Support compliance with applicable data privacy regulations and partner with the Tech team on security where needed. • Identify and escalate operational risks early and perform other duties as assigned to support projects and departmental needs. What you’ll bring to the role: Critical Competencies: R&D Operations & Insights • Partner with Engineering and R&D stakeholders to understand how work moves through the development lifecycle and identify opportunities to improve visibility, efficiency, and operational effectiveness. • Develop and maintain meaningful reporting and metrics around R&D activity, capacity, delivery, workflow health, and team performance. • Analyze operational and development data to identify trends, bottlenecks, inconsistencies, and opportunities for improvement. • Translate data and operational findings into clear, useful insights for both technical teams and business stakeholders. • Help improve the quality and consistency of development and ticketing data so that reporting and AI-generated insights are reliable. • Support process improvement initiatives across R&D without adding unnecessary administrative burden to engineering teams. • Comfortable working with structured business and operational data using SQL, spreadsheets, reporting platforms, or BI tools. AI Enablement & Agent Development • Hands-on experience using generative AI tools to solve business or operational problems. • Experience building, configuring, training, testing, and refining AI agents or AI-assisted workflows. • Strong working knowledge of Claude and the ability to build and iteratively improve Claude-based projects, agents, workflows, or other AI solutions. • Ability to evaluate AI-generated outputs for accuracy, usefulness, consistency, and appropriate use of source context. • Experience improving AI performance through prompt design, instructions, context management, source-data refinement, testing, and feedback. • Understanding of hallucination risk and practical approaches for improving the reliability of AI-generated outputs. • Experience designing AI workflows that incorporate human review or approval where appropriate. • Ability to identify practical opportunities where AI can reduce manual work, improve reporting, surface useful insights, or support better decision-making. • Comfortable experimenting with new AI capabilities and determining whether they provide meaningful business value. Engineering & R&D Collaboration • Experience working closely with software engineering, product, technical, or R&D teams. • Ability to translate between technical and non-technical stakeholders and turn business needs into clear requirements, workflows, reporting, or process improvements. • Strong stakeholder-management skills, including the ability to influence, coordinate, and move work forward without direct authority. • Comfortable asking technical teams for information, clarification, or assistance while respecting competing priorities and engineering ownership. • Able to independently investigate problems, gather available context, and clearly define what assistance is needed before escalating to Engineering. • Comfortable operating as a collaborative peer within a technical environment rather than as a people manager. Professional Skills • Well organized and dependable, with the ability to manage multiple priorities and bring structure to ambiguous problems. • Excellent interpersonal and written communication skills, with the ability to build productive working relationships across technical and business teams. • Able to communicate technical concepts and analytical findings clearly to both technical and non-technical audiences. • High level of integrity, attention to detail, and accuracy. • Curious and comfortable learning unfamiliar systems, tools, and business processes. • Comfortable working in a fast-moving environment with a bias toward practical, measurable results. • Collaborative, resourceful, and comfortable working independently while knowing when to involve subject-matter experts. Preferred and Differentiated Competencies: • Experience with Atlassian products such as Jira and Confluence. • Experience with Atlassian Rovo, Rovo agents, or similar AI-enabled work-management platforms. • Experience with engineering analytics, developer productivity, or R&D reporting platforms such as Jellyfish or similar tools. • Experience developing metrics or reporting around software delivery, engineering capacity, cycle time, throughput, planning, or development workflows. • Experience with Claude and other major generative AI platforms. • Experience with AI agent builders, workflow automation platforms, or low-code/no-code automation tools. • SQL experience and familiarity working with relational or structured datasets. • Experience with BI an