Design and scale LLM-powered agents operating in live cloud environments. Build production-grade services that integrate AI components with deterministic systems. Optimize performance of distributed workloads.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
š» Job Title: AI Engineer / Software Engineer (Backend)
š° Salary: up to Ā£135k / ā¬155k
š Equity: up to 0.2%, currently valued at $250k (aiming to ~x10 in 3-4 years)
š Location: fully remote anywhere in Europe (quarterly team get togethers)
š Company: Cyber Security SaaS start-up building AI agents to identify and resolve cloud vulnerabilities
š„ Team: ~40
šø Funding: $30m+ (Series A)
The company
This rapidly-growing, AI-native cybersecurity startup is building autonomous agents that investigate and remediate cloud vulnerabilities at enterprise scale. Their agents donāt just classify alerts - they:
- Read vulnerability documentation
- Run tools against real infrastructure
- Determine exploitability in context
- Assess business impact
- Recommend concrete remediation
š Their customers are seeing 80-90% reduction in vulnerability noise. That's huge.
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The role
This is not an ML / AI research role. It's all about productionising AI systems that actually work in the real world, ingesting enormous volumes of data and solving complex problems at enterprise scale.
𫵠What youāll be doing:
- Designing and scaling LLM-powered agents operating in live cloud environments
- Architecting backend systems to support agent orchestration, evaluation and reliability
- Building production-grade services that integrate AI components with deterministic systems
- Making AI systems more reliable, observable and cost-efficient
- Optimising performance of distributed workloads
- Shipping customer-facing remediation tooling
- Expanding into AppSec (static analysis + software vulnerabilities)
- Working in a greenfield, high-ownership environment
š¤ Youāll be expected to think in terms of:
- āHow does this behave in production?ā
- āHow do we evaluate and monitor agent performance?ā
- āWhat are the failure modes?ā
- āHow does this integrate cleanly with the rest of the stack?ā
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ā Must have requirements:
- Strong software engineering background and a mastery of Python
- Experience building and shipping LLMs or AI-agent systems in production
- Deep understanding of distributed systems and scalability trade-offs
- Deep AWS expertise
- Proven ability to operate in ambiguity and build from first principles
š Bonus points for:
- Previous experience in big tech/companies moving massive data volumes as well as rapidly scaling start-ups
- Founding engineer experience
- Domain expertise in AI and/or Cyber Security, ideally understanding of vulnerability management
- Experience with agent frameworks (LangChain, LlamaIndex, DSPy, custom orchestration layers)
- Evaluation frameworks, tracing, observability for LLM systems
- Experience scaling systems under real load
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