VP of Engineering

SiteZeus • Tampa, FL

22h • 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 SiteZeus’s location intelligence platform, including bringing reliable AI capabilities into the product. This is a builder-leader role rather than a manager-of-managers position: the VP stays close to architecture, pull requests, debugging, and occasional implementation while coaching engineers and partnering directly with the CEO to connect technical decisions to customer and business outcomes.

The VP will define platform and architecture standards; architect and operate LLM-backed product features over proprietary location intelligence data; and strengthen security, testing, observability, deployment, incident response, and recovery practices. The position will improve the team’s AI-native development workflow, own planning and execution across the engineering toolchain, and lead hiring, development, accountability, and executive communication.

The role oversees SiteZeus product engineers and works closely with the Director of Applied Science, who leads research and applied machine learning for forecasting models and does not report to this position. The two leaders will connect model research with dependable production software. The team uses Linear, GitHub, Slack, and Gong, and AI coding agents are already part of everyday engineering.

This is a full-time remote role within the United States, with Tampa-area candidates preferred and occasional in-person time in Tampa as needed. The position reports to the CEO. Base salary is $150,000–$170,000 per year, depending on experience.

Requirements

• 10+ years building and operating software.
• 4+ years directly leading engineers.
• Experience designing systems, shipping code, owning incidents, and remaining technically engaged while leading a compact, high-performing team.
• Firsthand experience shipping and supporting LLM-powered features in production, including retrieval/RAG, embeddings, tool use, evaluation harnesses, prompt/context design, failure modes, security, latency, and inference cost.
• Active use of modern coding agents and understanding of their strengths, limitations, hallucination risks, review requirements, and controls for responsible team use.
• Strong architectural and operational judgment in data-heavy SaaS systems, APIs, pipelines, production testing, observability, CI/CD, and secure cloud infrastructure.
• Ability to balance 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 when integrating research into production.
• Clear communication with executive stakeholders and ability to thrive with autonomy, ownership, and lightweight process.
• All U.S.-based applicants must be legally authorized to work in the United States; employment visa sponsorship is not available.

Nice To Haves

• Experience with 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 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 with production agents working securely over customer-specific data, sophisticated evaluations, AI-enhanced CI/code review, or applied computer vision and decision-support pipelines.

Responsibilities

• Define and evolve pragmatic standards for services, APIs, data flows, security boundaries, deployment topology, and interfaces.
• Make build-versus-buy decisions for the AI stack and guide platform modernization while maintaining 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 for AI capabilities.
• Improve security, secrets management, observability, CI/CD, automated and end-to-end testing, data integrity, incident response, and recovery readiness with practices appropriate for a small team.
• Improve engineers’ use of coding agents, AI-assisted reviews, repository context, automated checks, and CI gates; define boundaries for autonomous changes and human review, and measure productivity, technical debt, and tooling cost.
• 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 shipped work’s impact.
• Hire, level, mentor, and develop engineers; establish accountability and growth paths.
• Partner with the CEO to explain technical tradeoffs, risks, investments, capacity, and the case for additional headcount.
• Stay technically engaged through architecture, pull requests, debugging, and occasional hands-on implementation.
• Partner with the Director of Applied Science to turn research and machine-learning advances into dependable customer-facing capabilities.
• Guide platform upgrades or migrations while continuing to serve customers.

Benefits

• Unlimited PTO that the company encourages employees to use.
• Flexible work hours.
• 401(k).
• Health, vision, and dental benefits, plus other traditional benefits for U.S.-based employees.
• Paid parental leave.
• Paid monthly community service time.
• Paid company summits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed