VP, AI Platform

The Opportunity
Engine is scaling fast: 1,000+ employees, moving upmarket, and using AI to fuel growth. The proof is already in production: 95.2% of merged production code is AI written, we ship roughly 1,300 pull requests a week (up from 800 a year ago, with fewer engineers), and we have restructured teams from 7 engineers to 5 with a gated path to 3. This is one of the most AI mature engineering organizations anywhere, and it is about to go further.

To do that coherently, Engine is standing up an AI native operating model with three coequal foundations: AI Platform builds the technical rails, Applied AI ships workflow redesign embedded in every domain, and Workforce Transformation updates the operating system. You lead the first.

AI Platform is what everything runs on. Without a single, trusted stack, every team forks its own plumbing and no one can measure or govern what ships. You will not be starting from zero: you inherit a founding team that includes a Principal Architect, a Principal Engineer dedicated to the AI harness, staff level product operations and analytics, and open reqs in flight. As the founding VP, you set the technical strategy, build hands on in the early phase, and scale the org through a Director and principal engineers as adoption proves out. You sit on The Triad with the Senior Director, Applied AI and the Senior Director, Workforce Transformation, prioritizing the enterprise AI roadmap and resolving cross domain calls.

What You'll Own
AI Platform core. The LLM gateway, agent framework, prompt and agent libraries, and evaluation infrastructure: the rails every Applied AI team and every engineer builds on.
Developer experience and the Velocity program. The AI harness, coding agents, testing and autonomy frameworks, and the coverage gates that unlock code merging without human review. This group owns Engine's product delivery cycle time number and is the engine that turns 95.2% AI written code into true autonomy. It is the crown jewel of the role.
Product operations, rebuilt as software. We believe product operations becomes an AI native function. This team builds and runs that software: instrumented cadences, automated reporting, gap analysis, and the PD maturity scorecard. It runs the machinery of the PDLC, not the judgment inside it. An AI Product Ops Lead reports into this role.
Automation engineering and APIs. Shared APIs, orchestration, and integrations: the rails every automation across Engine uses.
AI data infrastructure. The AI consumable layer: retrieval and RAG infrastructure, context pipelines for agents and the knowledge base, evaluation datasets, agent memory, and usage telemetry. This layer consumes Engine's data warehouse through governed interfaces; the warehouse, pipelines, and data quality remain with the Data organization.
Observability, security, and cost. Monitoring, cost controls, and the security posture for platform AI, built in by default and in partnership with Security and IT.
One stack, enterprise wide. Hold the line on a single shared platform that serves all domains, no forks and no parallel stacks, while meeting the real delivery needs of the Applied AI teams that build on it.
Team. Grow the platform org as the roadmap proves out: a Director plus principal and staff engineers across agent platform, infrastructure, evaluation, observability, security, and developer experience.
Scope note: Engine's customer facing AI runtime (Prompt Execution Services and the MCP product layer) is owned by a dedicated AI Systems team inside product engineering, with SLAs and on call. AI Platform integrates with it at a defined interface. This role owns the internal platform, not customer traffic.

What You Bring
Experience. 15+ years in software or platform engineering, including 5+ years leading engineering teams or orgs and 8+ years building platforms or infrastructure other teams depend on. 3+ years hands on with production AI/ML: direct work with LLMs, agents, and evaluation. Given how new production LLM and agent systems are, we weight depth of shipped work over years in the space.
Platform and infrastructure leadership at scale. You have built and run shared platforms that many teams depend on, and made the build vs. buy and architecture calls that shaped an engineering org.
Deep AI systems expertise. Hands on command of LLMs, agents, orchestration, evaluation, and the production realities of AI: latency, cost, reliability, and safety.
Hands on building. You can personally architect and ship core platform services in the founding phase, not just direct others.
Executive influence and coequal leadership. You have partnered with executive peers on technical strategy and driven alignment across functions, comfortable owning a lane without absorbing others' mandates.
Engineering leadership. Proven ability to hire, level, and grow senior engineering talent and set a high craft bar.
Security and responsible AI judgment. You build security, privacy, and safe use controls into AI infrastructure by default.

Success in the First Six Months
The core rails are live and adopted: LLM gateway, agent framework, and evaluation infrastructure used by the first Applied AI teams and by engineering at large.
The Velocity program has a measured baseline: product delivery cycle time instrumented end to end, with the automated weekly scorecard publishing to leadership without manual effort.
The path to autonomy is open: coverage gates and testing frameworks live in the first pods, with entry criteria agreed for merging well covered code without human review.
The platform is the default way to build AI at Engine, with at least one flagship capability shipped on it and no competing bespoke stacks.
Cost controls and a security posture for platform AI are established with Security and IT.
The Triad operating rhythm is working, and the platform org (Director plus first principal and staff engineers) is growing on plan.