Data Science & AI / ML Integration Engineer

Technology Stack & Tooling

Tools and technologies you will use on live production builds:

What You Will Build & Deliver

Hands-on responsibilities during your tenure:

• Clean, preprocess, and analyze commercial structured and unstructured datasets with Pandas and NumPy.
• Build exploratory data visualization dashboards for business intelligence.
• Train, evaluate, and fine-tune supervised and unsupervised machine learning models.
• Integrate Large Language Model (LLM) APIs, embeddings, and Retrieval-Augmented Generation (RAG) into applications.
• Deploy models as lightweight REST APIs using Python FastAPI or Flask.
• Monitor model performance, data drift, and inference latency.

Who We Are Looking For

Prerequisites and profile expectations:

• Proficiency in Python and standard data science libraries (Pandas, NumPy, Scikit-learn).
• Solid mathematical understanding of statistics, probability, and linear algebra.
• Experience with data visualization tools (Matplotlib, Seaborn, or Plotly).
• Familiarity with REST APIs and basic backend integration concepts.
• Prior academic or personal project in Machine Learning or data analysis.