Description

You will build next-generation intelligent decision systems powering pricing, optimization, ranking, and personalization at scale.

Responsibilities

• Design and implement ML models for dynamic pricing, availability prediction, and real-time demand optimization.
• Develop and maintain data pipelines and feature stores supporting large-scale model training and inference.
• Leverage Bayesian inference and reinforcement learning to drive adaptive decision platforms.
• Build ranking and recommendation systems for personalization and supply visibility.
• Operationalize ML and LLM pipelines on platforms like Databricks / AWS for training, inference, and monitoring.
• Run A/B experiments and causal validation to measure direct business impact.

Required Skills

• 5–9 years of hands-on experience in Applied ML or Data Science.
• Strong proficiency in Python, PySpark, and SQL.
• Experience developing models for ranking, pricing, recommendation, or forecasting at scale.
• Hands-on with PyTorch or TensorFlow for real-world ML or DL use cases.
• Practical experience integrating LLM/GenAI workflows (LangChain, RAG, embeddings, Claude, GPT, SLMs) into production.
• Experience with Databricks, Spark, or SageMaker for distributed training and deployment.
• Familiarity with experiment platforms, MLflow, and model observability best practices.
• Proficiency with core tools including SciKit-learn, AWS, MongoDB, and Redis.

Key Skills

Education

Any Graduate