Machine Learning Engineer (GenAI & Big Data)

The Role We are seeking a mid-to-senior level Machine Learning Engineer to join our data team. This isn't just a "model building" role; we need someone who can handle massive datasets using PySpark and implement cutting-edge Generative AI solutions. You will be responsible for the end-to-end lifecycle of ML models, from data wrangling with Pandas and NumPy to deploying production-ready AI. Key Responsibilities • Scalable AI: Develop and deploy large-scale machine learning models and GenAI applications. • Data Orchestration: Use PySpark to process and transform massive datasets for model training and inference. • Algorithm Development: Apply advanced ML techniques and algorithms to solve complex business problems. • Full-Stack ML: Take ownership of the data pipeline—from initial exploration in Scikit-Learn to deep learning implementations in PyTorch. Qualifications Technical Must-Haves: • Big Data & Deep Learning: Proficiency in PySpark and PyTorch. • Generative AI: Hands-on experience implementing GenAI frameworks and LLM-based solutions. • Core Python Stack: Expert-level knowledge of Python, Pandas, NumPy, and Scikit-Learn. • Fundamentals: A strong grasp of probability, statistics, and core machine learning algorithms (Regression, Clustering, Neural Networks, etc.). Education & Experience: • Experience: Minimum of 3+ years in a dedicated Data Science or ML Engineering role. • Education: Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, or a related quantitative field. • Coding: High proficiency in Python (primary) or R.