AI Agent Engineer

About Us

We’re a venture-backed startup headquartered in downtown Palo Alto, preparing for a public launch later in 2026. Backed by leading institutional investors and strategic angels, we’re building a verified operating system designed to run at national scale.

Our platform brings together digital identity, secure communication, modern money movement, and intelligence-driven services in a single system. Built on modern financial rails and applied AI, it supports real-world use cases such as payments, remittances, and digital services – designed for reliability, security, and scale.

We’re starting with deployments across markets where modern infrastructure meets real operational demand, and expanding globally from there. Initial institutional deployments are underway, with commercial agreements in place and revenue already being realized.

If you’re motivated by building durable systems and shaping the next generation of digital infrastructure, we’d love to build with you.

We’re seeking an AI Agent Engineer to design, build, and operate intelligent agents that run inside Modveon’s Verified Operating System. This is a hands‑on, high‑impact role for an engineer who enjoys working across machine learning, systems, and product—turning large language models into reliable, governable, production‑grade agents.

This role is not about training a standalone chatbot. You’ll work with foundation models from the ecosystem, combine and adapt them for specific use cases, and build the surrounding systems required to safely deploy, version, monitor, and evolve AI agents over time. These agents will power real‑world workflows such as complaints handling, announcements, community management, and automated coordination—operating in regulated, high‑trust environments.

You’ll collaborate closely with backend, frontend, product, and design partners to build agent infrastructure and tooling, including interfaces that allow institutional users to configure, train, and manage agents themselves over time.

You’ll take end-to-end ownership of core AI agent domains, from initial design through production deployment and iteration. Scope and ownership will scale with experience.

• Design and implement AI agents built on top of one or more foundation models, tailored to specific product workflows and institutional use cases.
• Build systems to train, evaluate, version, and roll back agents safely as capabilities evolve.
• Manage upgrades to underlying model layers while maintaining agent behavior, performance, and reliability.
• Define and enforce rules around agent changes, including approval workflows and safeguards before new behaviors are released.

• Implement controls that ensure agent behavior is auditable, explainable, and aligned with product and institutional requirements.
• Build mechanisms for policy enforcement, human review, and staged rollout of agent capabilities.
• Ensure agents operating on sensitive data follow strong security, privacy, and compliance standards.
• Design guardrails that constrain agent behavior within clearly defined policy, permission, and capability boundaries.

• Partner with backend engineers to integrate agents into core platform services and workflows.
• Work with frontend engineers to build admin and control interfaces that allow operators to configure, train, and manage agents over time.
• Enable handoff of agent management to institutional users, while preserving platform-level safeguards.

• Design systems that allow multiple specialized agents to coexist (e.g., complaints handling, announcements, summarization), with clear boundaries and responsibilities.
• Monitor agent performance and behavior in production, using feedback loops to improve quality, accuracy, and usefulness over time.
• Leverage AI-assisted development tools to accelerate experimentation, iteration, and system evolution.

• Experience working with machine learning or AI systems in production, including model integration, evaluation, and iteration.
• Strong engineering fundamentals, with experience…