VP of Engineering

SiteZeus•Tampa, FL

10h•$150,000 - $170,000•Remote

About The Position

SiteZeus is hiring a hands-on VP of Engineering to lead a small, senior product engineering team operating AI-first. The role owns technical direction, production quality, and delivery for the location intelligence platform, including bringing reliable AI capabilities into the product. This is a builder-leader position: the VP remains engaged in architecture, code reviews, debugging, and occasional implementation while coaching engineers and partnering directly with the CEO to connect technical decisions to customer and business outcomes. The VP oversees the engineers building the SiteZeus product and works closely with the Director of Applied Science, who leads research and applied machine learning for forecasting models but does not report to this role. The VP helps bridge model research and production software so advances become dependable customer-facing capabilities. The team uses Linear, GitHub, Slack, and Gong, and AI coding agents are already part of everyday engineering. The position is full-time and remote within the United States, with Tampa-area candidates preferred and occasional in-person time in Tampa as needed. It reports to the CEO. The base salary range is $150,000–$170,000 per year, with final compensation depending on experience. Applicants must be legally authorized to work in the United States; employment visa sponsorship is unavailable. Degree requirements and preferences may be substituted for bona fide work history or relevant experience.

Requirements

10+ years building and operating software, including 4+ years directly leading engineers.
Experience designing systems, shipping code, owning incidents, and remaining technically engaged while leading a compact team.
Firsthand experience shipping and supporting LLM-powered features in real-world use, including retrieval/RAG, embeddings, tool use, evaluation harnesses, prompt and context design, failure modes, security, latency, and inference cost.
Active use of modern coding agents, with understanding of their strengths, limitations, hallucination risks, review requirements, and controls for responsible team use.
Strength in data-heavy SaaS systems, APIs, pipelines, production testing, observability, CI/CD, and secure cloud infrastructure, with sound judgment balancing speed, maintainability, performance, and complexity.
Comfort reading and reviewing .NET/C# back-end services and TypeScript/Vue front-end work on Azure, with an ongoing PostgreSQL migration; ability to collaborate across Python-based data and machine-learning work.
Experience with agent orchestration patterns and frameworks such as LangGraph and MCP-based tools.
Familiarity with mapping and geospatial data, spatial queries, Mapbox or similar platforms, and performance considerations for large datasets.
Experience guiding meaningful platform upgrades or migrations while continuing to serve customers.
Clear communication with executive stakeholders and ability to thrive with autonomy, ownership, and lightweight process.
Legally authorized to work in the United States; SiteZeus cannot sponsor employment visas for this role.

Nice To Haves

Experience in location intelligence, retail site selection, trade areas, POI or mobility data, geospatial infrastructure such as PostGIS, or enterprise B2B SaaS.
Early-stage or founding-engineer experience, including establishing dependable engineering practices.
Experience delivering production agents securely over customer-specific data, sophisticated evaluations, AI-enhanced CI or code review, or applied computer vision and decision-support pipelines.
Tampa-area location is preferred.

Responsibilities

Define and evolve standards for services, APIs, data flows, security boundaries, deployment topology, and interfaces; make build-versus-buy decisions for the AI stack and guide modernization while preserving reliability and delivery pace.
Architect and operate LLM-backed capabilities over proprietary location intelligence data, including retrieval and embeddings, tool/function calling, structured outputs, agent orchestration, context management, authorization, guardrails, and evaluation; own production quality, latency, reliability, and unit economics.
Improve production engineering practices for security, secrets management, observability, CI/CD, automated and end-to-end testing, data integrity, incident response, and recovery readiness, using safeguards suited to a small team.
Improve AI-native software delivery practices, including coding agents, AI-assisted reviews, repository context, automated checks, and CI gates; set boundaries for autonomous changes and human review, ensure testable outcomes, and track productivity, technical debt, and tooling costs.
Own the engineering operating cadence across Linear, GitHub, Slack, and Gong; shape and sequence the backlog, connect customer conversations to priorities, define release readiness, and measure the impact of shipped work.
Hire, level, mentor, and develop engineers; establish accountability and growth paths; partner with the CEO on tradeoffs, risks, investments, capacity, and headcount needs.
Remain technically engaged through architecture, pull requests, debugging, and occasional hands-on implementation while coaching a compact, high-performing team.
Work with the Director of Applied Science to bring research and applied machine-learning advances into dependable production software.