AIML Engineer Financial Prediction & Quant Intelligence

HR POD - Hiring Talent Globally; Dubai; On-site; AED 300,000 - 600,000; Full time

Job summary

HR POD - Hiring Talent Globally is seeking a skilled ML/FinTech specialist to develop forecasting and market analysis models. You will build end‑to‑end pipelines, assess macro indicators, and translate signals into tradable insights within production systems.

The role emphasizes collaboration with traders, engineers, and analysts, applying NLP, LLMs, and computer vision to financial data, backtesting, and rapid deployment in cloud environments.

Qualifications

• Strong proficiency in Python with ML/AI focus.
• Hands-on experience in NLP, forecasting, and CV.
• Solid understanding of financial markets and indicators.
• Experience building end-to-end ML pipelines and deploying models.
• Familiarity with MLOps tools, Docker, FastAPI and cloud platforms.
• Background in fintech, algo trading, or financial analytics.
• Experience with LLMs, embeddings, RAG pipelines, transformers.
• Knowledge of backtesting frameworks or custom engines.
• Experience with distributed computing (Spark, Ray).

Responsibilities

• Develop linear and data-driven forecasting models for macro indicators.
• Build predictive models for on-chain metrics and market signals.
• Design datasets for expectation-based trading and forecasting tools.
• Collaborate with product and engineering to productionize models.
• Create rule-based and ML-driven technical indicators for trading strategies.
• Develop ML models for pattern recognition and volatility analysis.
• Conduct backtesting, feature engineering, and optimization.
• Convert signals into actionable trading insights with traders/analysts.
• Develop financial NLP models for realtime sentiment scoring.
• Evaluate market impact of news and social signals.
• Ingest, clean, rank and score text-based financial data.
• Integrate sentiment signals into forecasting models.
• Apply CV techniques to chart patterns and visual signals.
• Build CNN/ViT models for breakouts and head-and-shoulders patterns.
• Convert chart images to structured features for ML/quant models.
• Collaborate with data engineering to maintain chart datasets.

Skills

Python

NLP

ML pipelines

MLOps

Distributed computing

Fintech analytics

LLMs

Backtesting

Computer vision

Tools

Docker

FastAPI

MLflow

Weights & Biases

Cloud environments

Spark

Ray

Job description

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.