VP of Engineering-Peach Pilot

Hive Financial Systems•Atlanta, GA

4h•$200,000 - $280,000•Hybrid

About The Position

Peach Pilot is a multi-agent autonomous AI platform that does real work — executive briefings, real-time research, psychological profiling, compliance monitoring, and full software development. We have dozens of specialized agents today and the architecture to scale to hundreds. Our agents don’t wait for prompts. They think, coordinate, and deliver. Built on OpenClaw, our orchestration layer lets us deploy, manage, and scale agent teams that work alongside humans — not as chatbots, but as capable coworkers. But here’s what we’ve learned: the technology only works if the humans trust it. In regulated financial services, AI that moves fast without earning trust gets shut down. That’s why our architecture separates the agents that do the work from the governance layer that ensures every action is auditable, explainable, and human-supervised. We call this our dual-agent architecture — execution agents handle workflows while governance agents monitor compliance, flag risks, and route exceptions to human decision-makers through Mission Control. We need the engineering leader who understands that building trust is an architecture problem, not just a product problem. This is a foundational hire. You’ll be the first VP of Engineering at Peach Pilot, reporting directly to the CEO. There are no legacy systems to untangle, no bureaucratic layers to navigate, and no ambiguity about impact — the platform you build will be the platform. Why now: We have a working multi-agent system with dozens of specialized agents, a clear product roadmap through mid-2026, and infrastructure supported by a dedicated team covering infrastructure, IT, and modeling. What we don’t have is the engineering leader who will take this from early platform to scalable product. That’s you. Our advantage: Peach Pilot is a fully independent, funded startup — not a division or internal project. What gives us an unfair advantage is strategic access to sister companies spanning life insurance, payments processing, loan origination, and other financial systems businesses. Their real data, real systems, real processes, and real people accelerate our path to product-market fit so we can build and validate faster than any startup starting from cold outreach. Once we’ve proven the platform, we sell to the broader market. You’ll be building production AI for regulated financial services from day one, with the domain access that most startups spend years trying to negotiate. Why this matters: The multi-agent AI space is moving fast, but most companies are building demos. We’re building production systems where accuracy has compliance consequences, where bias detection isn’t optional, and where “hallucination” isn’t a punchline — it’s a regulatory risk. Financial services is a relationship-driven industry where AI can transform how organizations operate, from underwriting support to client management to compliance. That’s exactly why it’s the most interesting.

Requirements

10+ years in software engineering with 3+ years leading engineering teams (VP, Director, or Head of Engineering)
Production AI/ML experience — you’ve shipped models and AI systems that real users depend on, not just notebooks and POCs
Cloud-native architecture depth on Azure (or strong AWS/GCP with genuine interest in Azure)
Hands-on experience with vector databases and embeddings (Cosmos DB vector, pgvector, Pinecone, Weaviate, or similar)
Distributed systems expertise — event-driven architectures, message queues, microservices at scale
Team-building track record — you’ve grown engineering teams from small (3–5) to mid-size (15–25) and know the cultural inflection points along the way
Comfort as both architect and engineer — in the early months, you’ll review PRs, debug pipelines, and make schema decisions alongside your team
An intuition for trust-building in enterprise software — you understand that in regulated industries, how the system earns human confidence is as important as what it can do

Nice To Haves

Experience in financial services, fintech, or regulated industries where compliance isn’t a checkbox
Background in NLP/NER pipelines, knowledge graphs, or multi-agent AI systems
Familiarity with graph databases (Neo4j, Cosmos DB Gremlin) and real-time data processing
A 0-to-1 track record — you’ve built products from scratch, not just maintained existing ones
Experience with the Anthropic API, Claude, or Model Context Protocol (MCP)
Hands-on experience with OpenClaw — our agent orchestration layer runs on it, and familiarity with its architecture, skill system, and session management is a real advantage
Experience designing human-in-the-loop governance systems, real-time monitoring dashboards, or compliance automation for regulated environments

Responsibilities

Own architecture and delivery of a multi-agent orchestration system built on OpenClaw: agent routing, context sharing, cross-agent handoffs, and conflict resolution across dozens of specialized agents
Design and implement the dual-agent architecture: separate execution agents (that do work) from governance agents (that monitor, audit, and escalate), ensuring every sensitive action is auditable and human-supervised
Build Mission Control — the real-time governance dashboard where human operators monitor agent activity, review escalations, track SLAs, and maintain oversight of autonomous workflows
Scale the Organizational Context Graph (Cosmos DB vCore + vector search) — the persistent knowledge graph that captures institutional memory, organizational dynamics, and decision patterns to create compounding intelligence unique to each client
Design and ship ingestion pipelines processing 500+ news events/day with NER, embedding generation, deduplication (cosine similarity), and multi-source verification
Integrate and optimize across three LLM providers (Anthropic Claude, OpenAI GPT-4o, xAI Grok-4) with intelligent model routing and fallback
Ensure 99%+ uptime for systems where accuracy matters — no “move fast and break things” here
Grow engineering from ~3 to 12–15, hiring across backend, AI/ML, data, and infrastructure
Establish engineering culture from scratch: code review standards, testing practices, deployment pipelines, incident response, on-call rotations
Define sprint cadences, delivery metrics, and engineering OKRs that actually mean something
Create the kind of team that attracts people who build things that matter
Partner directly with the CEO on roadmap prioritization and technical feasibility
Make foundational architecture decisions: database choices, agent communication protocols, multi-tenant isolation patterns
Evaluate build-vs-buy across the stack — we’re opinionated about building what differentiates us and leveraging what doesn’t
Shape the technical vision for how AI agents collaborate at scale in regulated industries — where trust, governance, and audit trails are non-negotiable