Description

You will develop and deploy machine learning models for forecasting, classification, regression, and portfolio analysis.

Responsibilities

• Develop and deploy machine learning models for time-series forecasting, classification, regression, stock scoring, and portfolio analysis.
• Design and build LLM-powered chatbots for investment insights and customer support, implementing prompt engineering.
• Build Retrieval-Augmented Generation (RAG) frameworks and design Agentic AI architectures.
• Deploy models using MLOps best practices, ensuring scalability and monitoring.
• Collaborate with backend and frontend teams to integrate AI services via APIs.

Required Skills

• Proficiency in Python, SQL, and relevant data science tools.
• Strong experience in predictive modeling and statistical analysis.
• Proficiency in machine learning frameworks including SciKit-learn, TensorFlow, and PyTorch.
• Experience in time-series forecasting and financial data modeling.
• Knowledge of LLMs, RAG frameworks, and Agentic AI architectures.
• Ability to perform feature engineering using financial, market, and alternative data.
• Understanding of MLOps practices for deployment and monitoring.
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.

Key Skills

Education

Any Graduate