AIML Engineer Financial Prediction & Quant Intelligence

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.