Description

You will manage the end-to-end lifecycle of machine learning models and deployment architectures.

Responsibilities

• Implement machine learning algorithms including Decision Trees, Random Forest, and XGBoost.
• Develop and maintain Python REST APIs using Flask or FastAPI.
• Design and manage MLOps architecture, focusing on CI/CD, monitoring, and model registries.
• Apply statistical techniques and structured data modeling through EDA and hypothesis testing.
• Integrate LLM concepts such as RAG, prompting, few-shot prompting, and SFT into production workflows.

Required Skills

• 7+ years of experience in machine learning implementation.
• Proficiency in Python and developing REST APIs with FastAPI or Flask.
• Deep understanding of ML frameworks including PyTorch, TensorFlow, Keras, and Scikit-learn.
• Strong knowledge of ML algorithms and statistical modeling.
• Practical experience with MLOps components and deployment pipelines.
• Familiarity with Large Language Model (LLM) techniques.
• Degree in any field of study.

Key Skills

Education

Any Graduate