AI-Native Solution Engineer

Description: Role purpose

Own NavPro’s AI-native pre-sales execution end-to-end: improve sales ops efficiency, accelerate product–market fit learning, define solutions for complex fleet deals, and drive the loop from roadmap ? implementation ? measurable outcomes. Demos are a core lever, but the role is accountable for the full technical-commercial motion from discovery through launch readiness.

Key responsibilities

Run customer-facing NavPro demos:

• Lead demos end-to-end, tailored to the customer’s fleet workflow.
• Position value, handle objections, and keep the technical narrative accurate.

Own discovery ? solution narrative:

• Partner with Sales on discovery.
• Translate customer needs into a clear demo story and recommended solution approach.

Technical validation and execution support (AI-native):

• Validate integration feasibility quickly (auth, permissions, data flow, edge cases).
• Create lightweight prototypes or API proof-of-concepts when needed (for example, Postman/curl, small scripts).
• Provide clear technical guidance to Sales and Product on feasibility, scope, and implementation paths (without owning day-to-day customer support).

Maintain demo systems and assets:

• Maintain demo environments, scripts, assets, and an industry case library.
• Keep examples current with latest product capabilities and limitations.

Cross-functional alignment and feedback loop:

• Align with Product/Engineering on capability boundaries.
• Ensure external commitments are accurate.
• Capture common blockers and requirements from demos and feed into the product pipeline.

Product ownership for PMF (field-led):

• Own a clear PMF learning agenda for NavPro fleet workflows, grounded in sales motions and onboarding reality.
• Define and track PMF signals (activation, retention, expansion, usage depth, time-to-value, NPS/CSAT proxies) and review them regularly with Product.
• Turn deal-cycle and customer feedback into prioritized product bets with crisp problem statements, impact sizing, and acceptance criteria.
• Partner with Product Managers to validate solutions via pilots, pre/post metrics, and a closed-loop feedback process.
• Ensure learnings are captured in a repeatable playbook (what works, for whom, and why).

Knowledge sharer:

• Create internal enablement materials and run training for Sales/CS.

Use AI to accelerate work:

• Generate tailored demo scripts and talk tracks.
• Draft follow-ups, technical summaries, and next-step plans.
• Produce competitive and objection-handling notes.
• Turn call notes into structured product feedback.

Requirements:

• 2–6+ years in pre-sales, solutions consulting, implementation, technical enablement, or product specialist roles.
• Strong presentation and stakeholder management skills.
• Comfortable with integration concepts: APIs, auth, permissions, data mapping, webhooks, monitoring, incident paths.
• AI-forward working style: uses AI daily for preparation, synthesis, writing, and iteration.
• Technical execution requirement (one of):
• Can write basic code or scripts (Python/Node/SQL) for API tests, data checks, or automation.
• Or has an engineering background with hands-on debugging experience.
• Excellent writing: can produce crisp customer summaries and internal documentation.

What success looks like (first 90 days):

• A repeatable demo + discovery flow that improves conversion on complex fleet deals.
• A maintained demo library mapped to top industries, objections, and integration scenarios.
• Faster technical validation cycles (clear go/no-go for integrations and custom requests).
• A steady pipeline of well-formed product feedback from demos that Engineering can action.