VP of Engineering (AI)

Location: Fully remote.

Company

Builds a global SaaS practice-management platform for accounting, tax, and finance firms, serving tens of thousands of businesses through a distributed team of 400+ people across 40+ countries.

What you will do

• Lead the transformation of a 180+ person R&D organization into an AI-first engineering function.
• Own delivery performance, remove blockers, and improve planning, estimation, and execution confidence.
• Raise engineering quality standards while keeping processes simple and focused on outcomes.
• Improve product-to-development handoffs, workflows, code review, and the broader development lifecycle.
• Lead managers and partner with the CTO on architecture, organizational, and delivery decisions.
• Review engineering clusters and roadmaps, then implement a practical improvement plan during the first three months.

Requirements

• Experience leading engineering organizations of 100+ people at scale.
• Experience managing managers and leading through other engineering leaders.
• Track record of organizational transformation with measurable improvements linked to business outcomes.
• Practical understanding of AI-first software development and the engineering discipline it requires.
• Product-first mindset, strong delivery ownership, and the ability to make sound technical decisions.
• English: B2+ required for stakeholder and C-level communication.

Nice to have

• Experience leading an engineering organization through an AI-first transformation.
• Experience with cloud-based products and high-volume AWS environments.

Culture & Benefits

• Employment through a B2B contract.
• 30 paid days off per year and sick days as needed.
• Health and well-being support.
• Learning and development budget, including reimbursement for English lessons.
• Co-working space reimbursement and company-provided equipment.
• High autonomy, ownership, and the opportunity to influence a fast-growing global SaaS product.

Hiring process

• Initial onboarding includes reviewing engineering clusters, roadmaps, and operational pain points.
• By the end of the first month, present a clear proposal for improvements.
• During the following two months, drive measurable progress with clear communication across teams.