Founding AI Engineer - Production Systems

Requirements

• 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
• Exposure to C++ / Rust / Go
• Experience in distributed or performance-critical environments
• Comfort operating with ownership and minimal hand-holding
• Why This Role
• You'll build AI systems that directly impact research and infrastructure
• You'll work with engineers who argue about trade-offs - and care about getting them right
• You'll have real ownership from design to deployment

Benefits

Additional Information

NK Securities Research is a leading financial firm that leverages cutting-edge technology and sophisticated algorithms to trade the financial markets. Founded in 2011, we have gained invaluable experience in the field of High-Frequency Trading (HFT) across different asset classes. 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. What You'll Build 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

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