LLM / Agentic Systems Engineer

Job description

We’re starting a brand-new product from the ground up. No legacy, no predefined stack, no “this is how we’ve always done it”. We’re building a pragmatic LLM-based agent system - and we’re doing it properly. That means time for research, spikes, evaluation, iteration, and architectural thinking before committing to production code. This is not just prompt tinkering. This is about understanding how agentic systems behave - and designing them to work reliably in real-world conditions. We’re tackling a real-world problem with a clear path to market, backed by SafeQ Cloud - and you’ll also help shape and improve the in-house tools that power the product. What we’re experimenting with right now Some of these will stay. Some won’t. That’s the point. - RAG pipelines - Vector databases - LLM vendor integrations (OpenAI, Anthropic, Google…) - LangChain stacks - Evaluation frameworks (e.g. DeepEval) - Agent orchestration patterns - MCP servers - Observability and reliability of LLM outputs Your role: You won’t just “implement tickets”. You’ll: We could list the precise tech stack and final architecture… …but that’s the fun part: we don’t have it yet. Want to help define it? - Help define the...