You will own the agent platform, transforming frontier model calls into production-grade enterprise features for high-stakes risk management. As a founding engineer, you’ll build the orchestration, evals, and reliability infrastructure that allows AI agents to act as peers to domain experts, setting the standard for AI quality and safety at scale.
Founding Engineer, Agent Systems at Helmguard
Join Helmguard.ai as a Founding Engineer to build the agent-native risk infrastructure that world-leading enterprises in finance and healthcare rely on. You will own the agent platform, architecting the scaffolding and reliability systems that turn frontier AI into production-grade systems of action.
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Location
London, United Kingdom
Compensation
Not Disclosed
Company
Helmguard
Role overview
HelmGuard Technologies, Inc. provides an AI-native enterprise trust and risk assurance platform that functions as an “Enterprise AI Risk Operating System” for security, compliance, and operations teams.[1][3] The company’s platform consolidates risk, security, and compliance data into a unified intelligence layer and uses specialized, autonomous AI agents to execute tasks such as risk assessments, compliance mapping, third‑party/vendor risk management, incident and exposure detection, and customer assurance reporting.[1][3][5] By orchestrating AI agents across multiple risk domains—including cybersecurity, IT, legal, finance, and regulatory compliance—HelmGuard enables continuous monitoring, predictive exposure detection, and generation of stakeholder‑ready, evidence‑backed reports, helping enterprises make faster, clearer, and more accountable security and risk decisions at scale.[1][3][4][5]
What you will do
- Architect and build agent scaffolding including tool use, context management, sandboxing, and robust prompt-injection defenses for enterprise-grade security.
- Develop sophisticated evaluation infrastructure for high-stakes outputs, utilizing LLM-as-judge frameworks and regression testing to ensure peer-level correctness.
- Engineer reliability systems including custom retries, circuit breakers, and prompt versioning to turn experimental model outputs into dependable production actions.
Who this is a fit for
- Proven backend engineering experience in TypeScript with at least 1-2 years of shipping production-grade LLM features and multi-step agent orchestration.
- Strong systems thinking regarding asynchronous queues, idempotency, and the ability to curate datasets for evaluating fuzzy, high-stakes compliance policies.
- A self-starter comfortable owning AI quality end-to-end, possessing the technical conviction to say “no” when features don’t meet rigorous safety bars.
Why this role is remarkable
- Join a high-growth startup that achieved seven-figure revenue months after launch, backed by tier-one investors and tech heavyweights from SpaceXAI and Palantir.
- Experience the outsize impact and influence of a founding-team role, with significant pre-Series A equity upside and a culture of radical ownership.
- Work at the extreme frontier of AI, pushing APIs so hard you’ll collaborate with labs like Anthropic to resolve core engine bugs.
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