About the role
Must Have :
Programming: Python (strong), SQL
ML Frameworks: Scikit‑learn, TensorFlow / PyTorch
GenAI / LLMs: OpenAI / Azure OpenAI, LangChain, vector databases
Data Engineering: ETL pipelines, data modeling, data validation
Cloud: Azure / AWS / GCP (at least one)
Design, develop, and deploy machine learning models for prediction, classification, and optimization use cases
Apply supervised, unsupervised, and time‑series techniques
Evaluate models using appropriate metrics (accuracy, precision/recall, RMSE, Sharpe ratio, etc.)
Build and optimize LLM-based applications (chatbots, assistants, summarization, Q&A)
Implement Retrieval Augmented Generation (RAG) using vector databases
Work with prompt engineering, model evaluation, and LLM guardrails
Integrate LLMs with tools, APIs, and enterprise systems
Perform feature engineering and model tuning
Participate in code reviews and contribute to architecture decisions.
Experience with financial domain data (risk, portfolio, pricing, forecasting)
Knowledge of model evaluation frameworks and LLM‑as‑judge concepts
Exposure to Hugging Face, fine‑tuning, or open‑source LLMs
Experience with Spark / Databricks