Forward Deployed Solutions Engineer (AI Products)

BizzyCar is a B2B SaaS company transforming the automotive care experience for consumers, dealerships, and manufacturers. Our mission is to simplify car repairs, enabling consumers to resolve issues like recalls without leaving home or work. Through our automated platform, we streamline the process from manufacturers to dealers to customers, significantly reducing notification times and making repair scheduling seamless. Dealers benefit from increased repair revenue and strengthened customer relationships, while OEMs avoid costly fines.

As BizzyCar’s founding Forward Deployed Solutions Engineer (FDSE), you will help our customers implement, adopt, and maximize our most cutting edge solutions. We're hiring a curious professional to own product launches on the ground: configuring the product to each dealership's systems and operations, training their staff, and being the calm, credible presence in the building when a brand-new AI product starts automating processes at the dealership.

In this role, you will be assigned to and become a subject matter expert on a specific product being rolled out to dealerships. Your training will make you comfortable with the automotive retail industry, customer processes, BizzyCar, frontier AI technology, application layer AI technology. You will be expected to use your knowledge and skills to make your implemented solutions maximally impactful to our customers and achieve loyalty from customers through delight.

This is a net-new, customer-facing field role which is slated to expand and re-invent how BizzyCar goes to market which means you will have a hand in shaping the playbook everyone after you follows.

(*Please Note: This role is Hybrid requiring being in office a few days a month as well as 50-70% travel to client dealerships.)

• →Own dealer implementations end-to-end — from kickoff through go-live and early hypercare
• →Configure solutions to each rooftop: translate a dealer's actual service-drive workflow and tech stack into product configuration through structured discovery and engineering team collaboration
• →Validate that customer, availability, recall, and RO data flow correctly across systems before go-live
• →Train non-technical dealership staff to full adoption, and manage the change along the way
• →Run the implementation like a project — own the timeline, the stakeholders, and the go-live date
• →Troubleshoot live: reproduce issues, read logs, tell a configuration problem from a product bug, and write clean repro steps
• →Know what to fix yourself versus what to escalate, and package escalations cleanly for engineering
• →Write the runbooks and SOPs that let this be repeatable — for the next dealer, and for the support team that eventually takes over steady-state
• →Be the feedback loop — route what you learn on-site back to product, reliably and specifically

• Hands-on integration & configuration experience — you've wired a product to external systems via APIs, webhooks, or config tools and validated it end-to-end
• Troubleshooting & log diagnosis — comfortable reproducing issues, reading logs, and isolating a config problem from a product bug
• Experience delivering customer training or enablement, ideally to non-technical audiences
• Implementation or deployment project management — you've run a kickoff-to-go-live timeline and owned the stakeholders in it
• Comfort validating and verifying data across systems (e.g., confirming records, statuses, or availability sync correctly)

• Discovery and workflow-mapping experience — translating a customer's process into product configuration
• Issue triage and escalation experience in a customer-facing technical role
• Light scripting or SQL — enough to query data or script a quick validation (a plus, not a requirement)
• Experience writing runbooks, SOPs, or setup documentation for handoff to a support team

You don't need deep expertise in all of these on day one, but familiarity with several will help you ramp fast:

• SaaS implementation and deployment methodology — onboarding lifecycle, go-live, hypercare
• APIs and integration patterns — REST, webhooks, authentication, and how systems talk to each other
• Automotive retail or fixed-ops experience — the service drive, repair orders, recalls, and the dealership's daily rhythm
• The DMS and scheduling ecosystem — e.g., CDK, Reynolds, Tekion, Xtime, myKaarma
• Data structure and validation concepts
• Support and escalation processes — tiers, SLAs, and clean handoffs
• Change management or adoption principles
• Security and compliance basics — PII handling, call-recording consent

• Ownership — you see things through to go-live, not just to the end of your task list
• Bias to action and resourcefulness — you unblock yourself on-site instead of waiting on someone else
• Learning agility — you can ramp fast on a product that's still being built and changing under you
• Adaptability — you're comfortable with ambiguity; this is an early product, there's no playbook yet, and things will break
• Structured and detail-oriented — implementations are checklists, and a missed step can cause a go-live to fail
• A calm troubleshooting temperament — you can debug live in front of a customer without losing the room
• Empathetic change management — you can move skeptical service staff toward trusting an AI to take their calls
• Feedback-loop discipline — you reliably route what you learn in the field back to product
• Low-ego collaboration — you work easily across engineering, support, GTM, and our partner team