AI Engineer

NK Securities Research; Gurugram District; On-site; INR 1,800,000 - 2,700,000; Full time

Benefits offered by this job

Competitive salary package

Dynamic work environment

Career growth and development

Catered breakfast and lunch

Monthly team dinners

Annual international and domestic team trips

Job summary

A leading financial firm in Gurugram seeks experienced engineers to develop and deploy AI systems that enhance research speed and efficiency. The role involves shipping models with strict performance metrics and collaborating with quant researchers to integrate AI into existing workflows. Candidates should have experience in deploying AI systems in production and a solid grasp of system design. This position offers a competitive salary and a dynamic work environment focused on professional growth.

Qualifications

• 1–5 years of experience deploying AI systems in production.
• Familiarity with transformers, embeddings, or LLM deployment.
• Strong thinking around system design and performance trade-offs.

Responsibilities

• Ship AI models meeting latency and reliability expectations.
• Implement structured RAG and embedding pipelines.
• Build tools speeding up research and development.
• Plug AI into data-heavy workflows without hurting performance.
• Work within a low-latency architecture and profile bottlenecks.

Skills

System design and performance trade-offs

Deploying AI systems in production

Familiarity with transformers and embeddings

C++ / Rust / Go exposure

Tools

C++

Rust

Go

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.

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

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:

• Competitive salary package.
• A dynamic, high‑performance, and collaborative work environment.
• Strong focus on career growth and development.
• Catered breakfast and lunch.
• Monthly team dinners.
• Annual international and domestic team trips.