Founding AI Engineer Production Systems
Skills
About This Role
Overview
We re looking for engineers who can take AI work beyond experiments and make it hold up in production.
You ll work closely with quant researchers and infra engineers to build AI systems that actually get used improving research speed and internal tooling without slowing down the core stack.
We value engineers who think about trade-offs, test what they build, and care about how things run in production.
Production AI
- Ship models that meet defined latency and reliability expectation
- Add monitoring, rollback, and guardrails before anything goes live
- Optimise inference across CPU/GPU environments when it matters
Integration into Real Systems
- Plug AI into data-heavy workflows without hurting performance
- Work within existing low-latency architecture instead of fighting it
- Profile and remove bottlenecks rather than guessing
AI for Engineers & Researchers
- Build tools that genuinely speed up research and development
- Improve code understanding, review workflows, and internal knowledge retrieval
- Keep systems auditable and predictable
LLM & Retrieval Systems
- Implement structured RAG and embedding pipelines with validation in place
- Create safe integration layers between models and internal systems
Performance & Standards
- Track latency, drift, and stability not just accuracy
- Build observability into everything you ship
- Help raise the bar for how AI is engineered here
What We re Looking For
- Strong Python fundamentals
- Clear thinking around system design and performance trade-offs
- Experience deploying AI systems in production (1 5 years is typical)
- Familiarity with transformers, embeddings, or LLM deployment
- Nice to have:
- Exposure to C++ / Rust / Go
- Experience in distributed or performance-critical environments
- Comfort operating with ownership and minimal hand-holding
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