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
π» Job Title: AI Engineer / Software Engineer (Backend)
π° Salary: up to Β£135k / β¬155k
π Equity: up to 0.2%, currently valued at $250k (aiming to ~x10 in 3-4 years)
π Location: fully remote anywhere in Europe (quarterly team get togethers)
π Company: Cyber Security SaaS start-up building AI agents to identify and resolve cloud vulnerabilities
π₯ Team: ~40
πΈ 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 donβt just classify alerts - they:
- Read vulnerability documentation
- Run tools against real infrastructure
- Determine exploitability in context
- Assess business impact
- Recommend concrete remediation
π 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.
π«΅ What youβll 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
π€ Youβll be expected to think in terms of:
- βHow does this behave in production?β
- βHow do we evaluate and monitor agent performance?β
- βWhat are the failure modes?β
- βHow does this integrate cleanly with the rest of the stack?β
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β 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
π 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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