AI​/ML Engineer - Financial Prediction & Quant Intelligence; Onsite AED

Requirements

• Strong proficiency in Python, PyTorch or Tensor Flow.
• Hands‑on experience in NLP, ML, time‑series forecasting, and computer vision.
• Solid understanding of financial markets, macroeconomic indicators, and technical analysis.
• Experience building end‑to‑end 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 (Back trader, Zipline, or custom engines).
• Experience with distributed computing (Spark, Ray).

• Develop linear and data‑driven forecasting models for macroeconomic indicators (GDP, CPI, employment).
• Build predictive models for on‑chain metrics, DVOL, volatility indices, and other market signals.
• Design data‑settled forecasting instruments for expectation‑based trading.
• Collaborate with product and engineering teams to integrate models into production systems.
• Create rule‑based and ML‑driven technical indicators for short‑ and mid‑term 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 (FinBERT‑style, transformer‑based, LLM‑based) real‑time 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 text‑based 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/ViT‑based 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 chart‑based datasets.