AI Engineer for Custom Recommendation Models
Skills Required
Description
Building intelligent recommendation systems takes a mix of science, creativity, and precision. We’re looking for an AI Engineer who can design, train, and optimize custom recommendation models that deliver accurate, personalized insights to users based on behavioral and contextual data.
The goal is to move beyond standard recommendation algorithms and develop something adaptive — a system that learns dynamically from user interactions, evolving as more data is introduced. You’ll work closely with our data science and engineering teams to turn raw datasets into models that can predict and personalize at scale.
Using Python, TensorFlow, and PyTorch, you’ll experiment with various architectures — from collaborative filtering and matrix factorization to deep neural networks and hybrid approaches. Model selection, hyperparameter tuning, and performance benchmarking will be part of your daily workflow.
You’ll also manage feature engineering pipelines, extracting meaningful signals from complex datasets using Pandas, NumPy, and related libraries. The ability to interpret data patterns and translate them into quantifiable model improvements will be key.
Performance evaluation will form the backbone of your development cycle. You’ll implement metrics such as precision, recall, F1-score, RMSE, and AUC to assess how effectively your models make predictions, ensuring they perform reliably in real-world environments.
Collaboration with backend ...