Description

You will design, build, and deploy Generative AI solutions and machine learning models into production environments.

Responsibilities

• Build Generative AI applications using Retrieval-Augmented Generation (RAG) and Agentic AI workflows.
• Develop, train, and optimize machine learning models to address specific business needs.
• Deploy models in production ensuring scalability, reliability, and maintainability.
• Build and maintain data pipelines using Snowflake Streams, Tasks, and UDFs.
• Create asynchronous, scalable Python APIs for model inference and integration.
• Monitor model performance and implement improvements based on metrics and feedback.

Required Skills

• 6+ years of hands-on experience in Machine Learning and Data Science.
• Strong programming proficiency in Python.
• Experience with ML and AI frameworks including TensorFlow, PyTorch, scikit-learn, and LangChain.
• Practical experience with LLM fine-tuning, prompt engineering, and evaluation strategies.
• Proven track record of deploying ML models in production environments.
• Experience building Python APIs (FastAPI, Gunicorn, Uvicorn) for model inference.
• Expertise in data preprocessing, feature engineering, and managing large-scale datasets.
• Solid foundation in statistical methods, data structures, and algorithms.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.

Preferred Skills

• Experience with Snowflake Cortex and Snowflake Data Pipelines.
• Hands-on work with AWS SageMaker, Bedrock, and other AWS cloud AI services.
• Knowledge of data visualization tools such as Matplotlib, Seaborn, or Tableau.

Key Skills

Education

Any Graduate