Description

You will develop and deploy machine learning models for forecasting, classification, and portfolio analysis, while building LLM-powered chatbots and RAG frameworks.

This role is on-site.

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 using prompt engineering.
• Build Retrieval-Augmented Generation (RAG) frameworks and design Agentic AI architectures.
• Deploy models using MLOps best practices to ensure scalability and monitoring.
• Collaborate with backend and frontend teams to integrate AI services via APIs.

Required Skills

• Proficiency in Python and SQL for data manipulation and analysis.
• Strong experience with predictive modeling and statistical analysis.
• Hands-on experience with machine learning frameworks: 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.

Preferred Skills

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.

Key Skills

Education

Any Graduate