AI Engineer
Role details
Job location
Tech stack
Job description
Most people are talking about AI. You're actually building it.
We're not looking for someone who just knows how to prompt a model or pass a certification. We're looking for an AI Engineer who lives for the "What if?", the kind of person whose browser tabs are a graveyard of GitHub repos and whose local machine is probably running a custom LLM right now just because you wanted to see if you could.
If your "hobby" projects look like most companies' "innovation" roadmaps, we want to talk to you.
The Mission
"At Company Z, we don't just implement tools; we create solutions. You will be our resident expert in the art of the possible. You'll bridge the gap between "cool demo" and "robust product," pushing the boundaries of what our tech stack can actually achieve."
Who You Are
- The Weekend Tinkerer: You have a home lab, a graveyard of half-finished experiments, or maybe a side project that's actually gained some traction.
- Deeply Technical: You aren't just a consumer of APIs. You understand the architecture, the latency trade-offs, and the plumbing underneath.
- Pragmatic Visionary: You know when to use a massive transformer and when a simple heuristic script is better. You build for impact, not just for the hype.
- Self-Taught & Curious: You didn't wait for a corporate training module to learn about RAG, agents, or fine-tuning. You learned it because you couldn't help yourself.
What You'll Do
- Design and deploy AI-driven features that solve real-world problems for our users.
- Architect systems that integrate LLMs, vector databases, and agentic workflows.
- Rapidly prototype "impossible" ideas to see if they stick.
- Stay three steps ahead of the industry, identifying which emerging tools are signals and which are just noise.
The Tech We Respect
We don't care if you learned this in a PhD program or in your basement. If you're fluent in these, you'll fit right in:
- Python / TypeScript | Go - any of these languages would be perfect
- Frameworks: LangChain, LlamaIndex, AutoGPT, or Haystack.
- Models: Experience with OpenAI, Anthropic, and the Open Source world (Llama, Mistral).
- Infrastructure: Vector DBs (Pinecone, Weaviate), deployment pipelines, and GPU orchestration.
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