Description :

- Build and maintain user modeling and personalization systems using behavioral and contextual data

- Develop ranking/recommendation models (learning-to-rank, hybrid approaches)

- Apply NLP techniques such as topic modeling, clustering, embeddings, and sentiment analysis

- Design data pipelines and feature engineering workflows

- Implement feedback loops and continuous model retraining pipelines

- Work on model evaluation, A/B testing, and performance optimization in production

Core Requirements :

- 5+ years of experience in applied machine learning (production systems)

- Strong foundation in supervised and unsupervised learning techniques

- Experience with recommendation/ranking systems

- Hands-on experience in NLP and large-scale text processing

- Proficiency in Python, Scikit-learn, PyTorch/TensorFlow

- Experience with data pipelines (Airflow, Spark, Kafka, etc.)

- Strong understanding of model evaluation and scalability challenges

- Experience with real-time ML systems

- Familiarity with vector databases and embedding-based retrieval

- Exposure to graph-based or relational modeling approaches