AI Engineer / Software Engineer (Backend)

wave group β€’ European Union
Remote
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AI Summary

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
Design and scale LLM-powered agents
Build production-grade services
Optimize performance of distributed workloads
Key Responsibilities
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)
Technical Skills Required
Python LLMs AI-agent systems Distributed systems Scalability trade-offs AWS Agent frameworks Evaluation frameworks Tracing Observability
Benefits & Perks
Salary up to Β£135k / €155k
Equity up to 0.2%
Fully remote work
Quarterly team get-togethers
Nice to Have
Previous experience in big tech/companies moving massive data volumes
Founding engineer experience
Domain expertise in AI and/or Cyber Security
Experience with agent frameworks
Evaluation frameworks, tracing, observability for LLM systems

Job Description


πŸ’» Job Title: AI Engineer / Software Engineer (Backend)

πŸ’° Salary: up to Β£135k / €155k

πŸ“ˆ Equity: up to 0.2%, currently valued at $250k (aiming to ~x10 in 3-4 years)

πŸ“ Location: fully remote anywhere in Europe (quarterly team get togethers)

πŸ” Company: Cyber Security SaaS start-up building AI agents to identify and resolve cloud vulnerabilities

πŸ‘₯ Team: ~40

πŸ’Έ 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 don’t just classify alerts - they:

  • Read vulnerability documentation
  • Run tools against real infrastructure
  • Determine exploitability in context
  • Assess business impact
  • Recommend concrete remediation


πŸ‘‰ Their customers are seeing 80-90% reduction in vulnerability noise. That's huge.


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.


🫡 What you’ll 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


πŸ€” You’ll be expected to think in terms of:

  • β€œHow does this behave in production?”
  • β€œHow do we evaluate and monitor agent performance?”
  • β€œWhat are the failure modes?”
  • β€œHow does this integrate cleanly with the rest of the stack?”


βœ… 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


πŸ‘ 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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