Build, train and deploy AI/ML models and applications for banking using Python, NLP, LLMs and cloud technologies to solve complex business problems and generate actionable insights.
Requirements :
- 10+ years in Data Science/ML Engineering with expertise in Python, NLP, LLMs, LangChain, FastAPI, Azure, TensorFlow/PyTorch, MLOps and production ML deployment.
- BFSI experience preferred.
Key Responsibilities :
- Architect and deploy end-to-end machine learning pipelines that automate complex financial processes and improve predictive accuracy for banking operations.
- Develop advanced predictive models to identify market trends, customer behavior patterns, and risk exposure, enabling stakeholders to make data-driven strategic decisions.
- Implement sophisticated NLP techniques to extract insights from unstructured financial documents, regulatory reports, and customer interaction logs.
- Lead the optimization of statistical models to ensure high performance, scalability, and compliance with stringent financial industry standards.
- Mentor junior data scientists and engineers by fostering a culture of technical excellence and rigorous analytical methodology within the team.
- Collaborate with IT and data engineering teams to ensure seamless integration of ML models into existing banking infrastructure and production environments.