AI Forward Deployed Engineer

Pam is the fastest-growing voice AI platform for auto dealerships. We handle millions of calls, book service appointments, and drive real revenue for almost 1000 dealerships across the US and Canada. We're backed by Autotech Ventures and partner with the biggest names in automotive, including Tekion, Dealer-FX, and CDK. We've powered $100M+ in repair orders and are building the AI operating layer for dealership customer communication. About the Role We're hiring a AI Forward Deployed Engineer to own AI outcomes for one strategic dealership group. This is not a traditional implementation role, support role, or solutions architect role. You'll learn Pam's platform deeply, work directly with the customer, and turn messy feedback into clear, shippable improvements to the AI agent. In the beginning, most of your work will be prompts, configuration, workflows, customer feedback, and agent behavior. You'll learn how Pam works, how our agents make decisions, what the platform supports today, and where the edge cases are. This is also not an ontology-first role. Over time, you may work on account-specific integrations, data flows, and platform extensions. But the day-one job is not deep data modeling or abstract platform architecture. The day-one job is customer feedback, prompt engineering, workflow judgment, and agent quality. The job is judgment and translation. A customer will ask for ten things. Some are simple: change the agent's behavior, update a prompt, adjust a workflow, improve a handoff. Some are real product gaps, but not things we should build today. Some sound reasonable but would make the agent worse. Your job is to understand the customer's feedback, identify what can safely improve the customer experience today, and work with Product and Engineering on anything that requires broader product changes. You do not own the customer roadmap alone; you own making sure the feedback becomes clear, actionable, and tied to better agent behavior. What You'll Do Own one strategic account deeply: Understand the customer's workflows, pain points, agent behavior, and success metrics. Tune AI behavior: Write, test, and improve prompts, instructions, workflows, guardrails, and agent configurations. Triage customer feedback: Break vague customer asks into manageable chunks: what we can support now, what needs a product change, and what should wait. Improve the agent fast: Ship practical prompt, config, and workflow changes that make the customer more successful without hurting other customers or overcomplicating the platform. Translate between customer and engineering: Turn real-world feedback into clear internal context for Product and Engineering. Protect the product: Push back when a requested change would create brittle behavior, hurt outcomes, or create one-off complexity. Measure quality: Use customer feedback, transcripts, outcomes, and the shared inbound eval/rubric to understand whether the agent is actually getting better. You Might Be a Good Fit If You Have 3-6 years of experience in software, AI, solutions engineering, implementation, product operations, or a technical customer-facing role. Have strong prompt engineering skills. This is the must-have. Are comfortable working directly with customers and explaining tradeoffs clearly. Can take messy feedback and turn it into a clear plan. Understand that not every customer request should become a product change. Can learn a complex platform quickly and develop strong product judgment over time. Are technical enough to understand workflows, APIs, data, and system behavior, even if you are not shipping production code every day. Use AI tools aggressively: Claude, Cursor, ChatGPT, or equivalent. Care more about live customer outcomes than beautiful internal plans. What Success Looks Like Week 1: You know the account, the current agent behavior, and the top customer pain points. You've shipped your first prompt, config, or workflow improvement. Month 1: You own the daily feedback loop for the account. Customer asks are getting triaged fast, agent changes are shipping weekly, and the customer feels technical momentum. Month 3: Agent quality is materially better against the shared inbound eval/rubric. You are independently turning customer feedback into scoped agent improvements, clear product asks, and crisp tradeoff notes for Product and Engineering. Month 6: You are designing account-specific integrations, data flows, and platform extensions where needed, without creating one-offs that burden other teams or degrade the broader platform. Bonus Points Experience with conversational AI, voice AI, agents, or LLM workflows. Experience in customer-facing engineering, solutions engineering, technical implementation, or product operations. Project management experience: timelines, owners, follow-ups, and tradeoff communication. Experience working with CRMs, dealership systems, or operational workflows. Light coding ability in TypeScript, SQL, or scripting languages. #J-18808-Ljbffr Pam