Performance Developer in England

We are building the infrastructure that makes autonomous AI safe for enterprise deployment. Not governance theatre. Not compliance checkboxes. Actual technical systems that can monitor, quantify, and govern AI agents operating with autonomy in production environments. If you have been following the trajectory from static models to agentic systems—and the corresponding explosion in risk surface area—you know why this matters now.

governr is the AI risk platform for regulated enterprises. We provide complete AI visibility, real-time risk evaluation and quantification, and audit-ready compliance documents for enterprises deploying agentic AI. We have built the industry’s most comprehensive AI risk assessment framework: We are currently in active discussions with tier-1 financial institutions and have secured design partners with leading firms navigating the shift from analytical AI to agentic systems.

The market timing is critical: enterprises are deploying agents at scale, regulators are demanding governance frameworks, and existing Third-Party Risk Management (TPRM) platforms have near-zero AI-risk depth. As an Agentic Developer at governr, you will build the core systems that monitor, analyse, and govern autonomous AI agents in production. This isn’t traditional software engineering with well-worn patterns.

• Agent Monitoring Infrastructure: Build real-time systems that track agent behaviour across risk factors including agent-to-agent interactions, privilege escalation attempts, emergent capability detection, and behavioural drift from baseline parameters.
• Risk Assessment Engine: Design algorithms that quantify AI risk in financial terms (£X exposure), map behaviours to regulatory requirements, and detect cascade failures before they propagate through multi-agent systems.
• Implement monitoring for agent communication protocols (Model Context Protocol, Agent2Agent, Agent Connect Protocol) as they mature, ensuring authenticated and logged inter-agent communications.
• Develop ML systems that identify when agents exhibit unexpected strategies, attempt unauthorized actions, or drift from intended behaviour—catching issues before regulators do.
• Build architectures that automatically capture audit trails, decision provenance, and compliance evidence without impacting agent performance.

Technical Depth:

• 3 - 12+ years building production systems at scale.
• Expert-level proficiency in Python, Rust, or Go (you write systems that can’t fail).
• Deep understanding of distributed systems, real-time data processing, and observability architectures.
• Production ML/AI experience: You have deployed models, debugged their failures, and built monitoring around them.
• System design mastery.

Domain Knowledge:

• Understanding of agent architectures: autonomous decision-making, goal-directed behaviour, tool use, memory systems.
• Familiarity with AI safety concepts: alignment, interpretability, robustness, adversarial examples.
• Experience with monitoring/observability: instrumentation, logging, tracing, alerting in complex systems.

Working Style:

• You ship to production regularly and own what you deploy.
• You write documentation that others can actually use.
• You thrive in ambiguity and define requirements through first principles.
• You communicate technical concepts clearly to non-technical stakeholders.

Publications or research:

• In multi-agent systems, reinforcement learning, AI safety, or agent architectures.
• Experience at AI labs (Anthropic, OpenAI, DeepMind) or leading AI research groups.
• Financial services, healthcare, or other domains with compliance requirements.
• Understanding of adversarial AI, prompt injection, or system security.
• Real-time systems experience: Trading systems, fraud detection, or other low-latency critical infrastructure.
• Open source contributions in relevant domains (AI frameworks, monitoring tools, security infrastructure).

Bonus Points (Nice to Have):

• Understanding of regulatory frameworks (EU AI Act, California AI, GDPR, DORA, FCA, OCC, FINRA guidance).
• You read AI safety papers for fun. You are intellectually curious about the intersection of AI capabilities and regulatory constraints.
• You find it genuinely interesting that the EU AI Act requires "human oversight" but doesn’t define what that means for autonomous agents.
• You believe AI agents will transform how organizations operate, but only if we solve the governance problem.

Autonomous AI is moving from research to production. 4T annual value from agentic AI. We are building it. Financial quantification of non-deterministic systems. You will help define what "AI governance" means.

Team Quality: Co-founders with deep financial services + AI expertise. Ayman Hindy, Marcel Cassard, and leading figures in AI, high frequency risk management and financial regulation.

Learning Curve: You will gain expertise in cutting-edge AI architectures, enterprise software, regulatory frameworks, and category creation simultaneously. Every system you build enables safe AI adoption for enterprises managing billions in assets.

Compensation: very competitive salary + equity in a fast-growing company with clear path to Series A.

Build the infrastructure that makes autonomous AI safe for society. Not many teams can say their technical work has direct regulatory impact. If you are excited about building unprecedented monitoring systems for autonomous agents, working at the intersection of AI safety and enterprise software, and defining an emerging category—let’s talk.

Include:

• Links to relevant work (code, papers, projects, or systems you have built).
• What you are currently reading/learning in the AI agent space.
• One technical challenge you would be excited to solve at governr.

Ready to build the governance layer for autonomous AI?