VP - Engineering

You will own technology and platform strategy for the company and lead engineering across AI, data, platform, DevOps, and infrastructure. You will report to the CEO and sit on the leadership team. This is a builder's seat, not a supervisory one. You will architect the next generation of an AI-native product stack, and you will do it hands-on with a small team. If you are looking for a role where you set direction and others write the code, this is not it.

The core responsibilities for the job include the following:

AI and product architecture:

• Own the GenAI and LLM solutions architecture end-to-end, including agentic and autonomous workflow design, multi-agent orchestration, data extraction, and intelligent automation.
• Provide hands-on architectural oversight of large language models, applied AI, and multi-layered deep learning products.
• Build intelligent document automation systems at the complexity of patent parsing, legal workflow automation, and structured decision intelligence.
• Take early-stage AI capability from prototype through to governed, production-grade enterprise deployment.

Technology and platform strategy:

• Define the technology vision, architecture, and long-term platform roadmap.
• Oversee architecture, design, and delivery of highly scalable enterprise systems.
• Lead platform engineering, product technology, DevOps, infrastructure, and quality engineering.
• Build cloud-native systems and own operational effectiveness, including uptime, production reliability, and cost.

Team and execution:

• Build, mentor, and scale a lean, high-performing engineering team from a small base.
• Set engineering standards for velocity, quality, and reliability across the product lifecycle.
• Establish a technology culture grounded in ownership, experimentation, and continuous learning.
• Work directly with the CEO and commercial leadership on product direction, customer requirements, and delivery commitments.

What success looks like:

• Production AI at scale. A world-class AI system built on GenAI, ML, and enterprise-grade data engineering, running reliably for demanding enterprise customers.
• A platform that scales. Architecture that holds as volume and customer count grow, and that stands in comparison with global leaders in this market.
• A team worth joining. An engineering organization with genuine ownership, high performance, and the ability to attract talent on its own reputation.

Requirements:

• 10 to 15 years in enterprise software engineering in B2B SaaS or technology-first companies.
• A proven record of taking early-stage AI and ML prototypes and scaling them into robust enterprise SaaS platforms, with multi-agent orchestration, complex workflows, and decision intelligence.