Job
Description
Key Responsibilities : - Design, develop, and deploy AI/ML models for data-driven applications. - Work with Neo4j (Graph Database) to model and analyze complex relationships in datasets. - Implement machine learning algorithms for recommendation systems, predictive analytics, and knowledge graphs. - Develop and optimize graph-based data pipelines and algorithms using Neo4j Cypher queries. - Process, clean, and analyze large datasets for feature engineering and model training. - Integrate ML models into scalable applications using Python, TensorFlow, PyTorch, or Scikit-learn. - Optimize model performance, accuracy, and scalability for production deployment. - Collaborate with data scientists, backend engineers, and product teams to enhance AI capabilities. Required Skills : - Machine Learning & Deep Learning : Hands-on with ML models, classification, clustering, NLP. - Neo4j & Graph Databases : Experience in graph data modeling, Cypher queries, and data relationships. - Programming : Proficiency in Python, TensorFlow, PyTorch, Scikit-learn. - Data Engineering : Experience with ETL pipelines, data preprocessing, and feature engineering. - Big Data & Cloud : Exposure to Spark, Kafka, AWS/GCP/Azure (Good to have). - MLOps & Deployment : Working knowledge of Docker, Kubernetes, CI/CD pipelines. - Strong Analytical & Problem-Solving Skills for AI-driven applications. Good to Have : - Experience with Graph Neural Networks (GNNs) and Graph Data Science. - Knowledge of NLP, Computer Vision, or Time-Series Analysis. - Exposure to automated ML pipelines and model monitoring.
