Investment Sciences Deep Learning Engineer at Intellismith

Highly motivated and skilled Deep Learning / AI Engineer with 3–5 years of hands-on experience, ideally within the financial domain. The ideal candidate should be passionate about building intelligent systems for time-series forecasting, risk modelling, and trading signal generation using modern deep learning techniques and other cutting-edge architectures. Key Responsibilities • Design, implement, and deploy deep learning models for financial applications such as stock price prediction, risk scoring, or algorithmic trading. • Optimize models for performance and deploy ability (quantization, pruning, efficient architecture design). • Evaluate models rigorously (metrics, validation, hyperparameter tuning, experiment tracking). • Deploy models and maintain workflows using MLOps best practices and cloud infrastructure (AWS preferred). • Apply advanced pattern recognition and data analysis techniques to extract meaningful insights from structured and unstructured financial data. • Conduct exploratory data analysis (EDA), feature engineering, and time-series analysis on financial datasets. • Stay up to date with the latest developments in deep learning, including papers and trends in LLMs, attention mechanisms, and financial NLP. • Collaborate with cross-functional teams including quants, data scientists, and product managers to translate business needs into technical solutions. • Optimize and scale models using cloud infrastructure (AWS, GCP, or Azure preferred). • Maintain code quality, back testing, and documentation best practices.

Investment Sciences Deep Learning Engineer

Emma

Responsibilities

Skills wanted:

Language(s) required:

Vikas Walia

Vikas Walia

About Intellismith:

mission:

intellismith.com/

Admin access needed