Founding AI Engineer – Production Systems

Actively Reviewing the Applications

NK Securities Research

India, Haryana, Gurgaon Full-Time On-site

Posted 10 hours ago • Apply by June 14, 2026

Job Description

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

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

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

What We Offer: