AI LLM / NLP Tech Lead (On-site) — Islamabad

AI LLM / NLP Tech Lead (On-site) — Islamabad Get AI-powered advice on this job and more exclusive features.

Start : ASAP

About VulSight Location :

Islamabad, Pakistan — on-site, full-time

Team :

Engineering (AI / Research)

Start : ASAP

VulSight is a Web3 security firm helping teams ship safely across

Move, Rust, and EVM

ecosystems. We’ve delivered hundreds of assessments in traditional cybersecurity and Web3, and we’re now building

NLP / LLM-driven agents

that reason over code, audits, and on-chain data to catch bugs earlier and accelerate reviews.

The Role You’ll own our AI roadmap end-to-end— research → prototyping → productization → team leadership . Your focus is on

LLMs, NLP, and AI agents

(tool use, planning, retrieval, evaluation) grounded in solid

ML fundamentals . Blockchain / security knowledge is

a plus.

What you’ll do

Design & ship agentic systems

that reason over smart-contract code (Move / Solidity), diffs, on-chain data, and prior audits; perform retrieval, analysis, test generation, and triage.

Build

RAG pipelines

(indexing, chunking, retrievers), long-context strategies, tool calling, and multi-step planning (e.g., LangGraph-style flows).

Own

LLM evaluation : golden sets, style metrics, rubric-based grading, and production feedback loops (hallucination detection, safety, guardrails).

Lead

prompt engineering

and

model customization

(SFT, LoRA / QLoRA, instruction tuning, RLAIF / RLHF where appropriate).

Stand up

inference infrastructure

(vLLM / TGI / Triton), latency & cost optimization, caching, and observability (tracing, telemetry, error budgets).

Curate

high-quality datasets

from public repos, audits, and on-chain sources; build labeling and synthetic-data pipelines.

Collaborate with security researchers to turn workflows into

auditor-grade copilots

(invariant mining, property / test generation, patch suggestions).

Hire, mentor, and lead

a small on-site AI team; drive roadmap, rituals, and delivery quality.

Must-have qualifications

Strong Python

and at least one major

DL framework

(PyTorch preferred).

4–8+ years in ML / AI; 2+ years owning end-to-end ML / LLM projects or leading small teams.

Hands-on with

LLM application stacks : retrieval (FAISS / Milvus / pgvector), embeddings, tool use / function-calling, streaming, telemetry.

Experience with

agent orchestration

(e.g., LangGraph, LangChain, LlamaIndex—or custom graphs) and productionizing LLM apps.

Solid

NLP fundamentals

(tokenization, sequence modeling, evaluation), plus

prompting & instruction-tuning

best practices.

MLOps / inference : serving (vLLM / TGI), batching, quantization, containerization, CI / CD for models, and experiment tracking.

Clear product sense : turning fuzzy research ideas into shippable features with measurable impact.

Nice to have

Blockchain / Web3

familiarity (Move, Solidity, Rust), static / dynamic analysis, or prior security / audit exposure.

Program analysis / compilers / AST work; code-intelligence or LLM-for-code experience.

Fine-tuning at scale (LoRA, PEFT), dataset distillation, or RLHF / RLAIF pipelines.

Systems performance chops (CUDA / Triton, vectorization) or search / retrieval research.

Experience with

on-chain data tooling

(Aptos / Sui / EVM nodes, indexers, subgraphs).

Why join

Real users, real problems :

your work lands in auditors’ hands immediately.

Ownership :

greenfield architecture with the mandate to do it right.

Learning loop :

tight feedback with senior security researchers and real codebases.

Compensation Competitive salary

• equity . Hardware and learning budget provided.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Blockchain Services

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