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Principal AI – Large Language Models Expert

UNEY
Dubai, UAE
Full Time
Lead
3 days ago
Transformer architecturesLarge scale pre trainingParameter efficient fine tuningModel compressionDataset engineeringLLM evaluation
Free

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Transformer architecturesLarge scale pre trainingParameter efficient fine tuning
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Role Overview

  • We are seeking a Principal AI – Large Language Model (LLM) Expert to lead the design, training, optimization, and deployment of language models across our privacy first security platform.
  • You will bridge cutting edge LLM research with privacy preserving constraints and real world security requirements.

Key Responsibilities

  • LLM Architecture & Research: Lead research and implementation of Transformer based architectures optimized for security classification tasks.
  • Pre training & Foundation Models: Optimize training strategies, distributed training across GPUs/TPUs, and compute efficiency.
  • Fine tuning & Adaptation: Own fine tuning strategies for downstream security tasks: phishing detection, BEC classification, malware indicators, policy violation detection.
  • Datasets & Data Quality: Design dataset generation and curation pipelines that preserve privacy while maintaining threat diversity.
  • Small Language Models & Edge Deployment: Develop efficient SLMs via quantization, pruning, and distillation for customer environments and edge devices.
  • Evaluation & Testing: Build rigorous evaluation frameworks specific to security: adversarial robustness, false positive rates in production, attack coverage.
  • Cross functional Collaboration: Partner with Product to define LLM driven features and translate requirements into ML problems.

Core Technical Expertise

  • Transformer architectures: BERT, GPT, T5, LLaMA, emerging models
  • Large scale pre training: data pipeline design, training efficiency, convergence optimization, compute cost management
  • Parameter efficient fine tuning: LoRA, adapters, prefix tuning, prompt tuning
  • Model compression & efficiency: quantization (INT8, FP8), pruning, knowledge distillation, low rank factorization
  • Dataset engineering: scalable data pipelines, quality assurance, privacy aware annotation, synthetic data generation
  • LLM evaluation & testing: task specific metrics, adversarial testing, bias assessment, robustness evaluation

Required Qualifications (Must haves)

  • PhD in ML, NLP, CS, or related field; or equivalent industry experience (10+ years at top tier tech, research labs, or specialized ML companies)
  • 10+ years in ML/AI with 5+ years focused on LLMs at scale (pre training, fine tuning, or deployment at 100M+ scale)
  • Proven hands on experience training, fine tuning, and deploying LLMs in production with measurable impact (latency, cost, accuracy tradeoffs)
  • Strong hands on expertise with PyTorch and distributed training frameworks (FSDP, DeepSpeed, Ray, etc.)
  • Experience in security, privacy, or safety critical domains (security analytics, threat intelligence, fraud detection, privacy preserving ML preferred)

Nice to Have

  • Publications in top tier ML/NLP/security venues
  • Production scale experience with privacy preserving ML
  • Security domain expertise: malware detection, phishing/BEC classification, threat intelligence, anomaly detection
  • MoE, RAG, multimodal, or cross lingual models experience
  • Edge/mobile deployment or embedded ML systems experience
  • Inference optimization frameworks: ONNX, TensorRT, vLLM, llama.cpp, Ollama
  • Alignment, RLHF, and responsible AI practices with production experience
  • Experience building or scaling ML teams from scratch

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