أدخل المسمى الوظيفي أو الكلمة الرئيسية

AIML Engineer Financial Prediction & Quant Intelligence

موقع الوظيفة:

دبي - الإمارات

الراتب شهرياً: لم يتم تقديمه من قبل صاحب العمل

تم النشر: 6 اغسطس 2026 (نُشرت قبل 20 يوم)

آخر موعد للتقديم: 4 نوفمبر 2026

عدد الوظائف الشاغرة: 1 عدد الوظائف الشاغرة

سجل للتقديم

Requirements:

Strong proficiency in Python PyTorch or TensorFlow.
Handson experience in NLP ML timeseries forecasting and computer vision.
Solid understanding of financial markets macroeconomic indicators and technical analysis.
Experience building endtoend ML pipelines and deploying models to production.
Familiarity with MLOps tools (MLflow W&B) Docker FastAPI and cloud environments.
Background in fintech algorithmic trading or financial analytics.
Experience with LLMs embeddings RAG pipelines and transformer architectures.
Knowledge of backtesting frameworks (Backtrader Zipline or custom engines).
Experience with distributed computing (Spark Ray).

Responsibilities:

Develop linear and datadriven forecasting models for macroeconomic indicators (GDP CPI employment).
Build predictive models for onchain metrics DVOL volatility indices and other market signals.
Design datasettled forecasting instruments for expectationbased trading.
Collaborate with product and engineering teams to integrate models into production systems.
Create rulebased and MLdriven technical indicators for short and midterm trading strategies.
Build ML models for pattern recognition volatility regime detection and microstructure analysis.
Conduct backtesting feature engineering and model optimization.
Work closely with traders and analysts to convert signals into actionable insights.
Develop financial NLP models (FinBERTstyle transformerbased LLMbased) for realtime sentiment scoring.
Build systems to evaluate market impact of news economic releases and social media signals.
Design pipelines for ingestion cleaning ranking and scoring of textbased financial data.
Integrate sentiment signals into trading and forecasting models.
Apply computer vision techniques to analyze candlestick charts indicators and visual market patterns.
Build CNN/ViTbased models to detect technical patterns (breakouts divergences head & shoulders etc.).
Convert chart images into structured features for ML and quant models.
Work with data engineering teams to generate and maintain chartbased datasets.