We're seeking a Machine Learning Engineer with strong data engineering expertise to build scalable real-time data pipelines and develop advanced ML models. This role involves collaborating with cross-functional teams to deliver innovative solutions. Key Responsibilities: - Data Engineering: Build and maintain real-time data pipelines and ETL workflows. Ensure data quality and integrity. - Machine Learning: Design, train, and optimize ML models for fraud prevention and personalization. - MLOps: Deploy, monitor, and maintain ML models in production using tools like Docker, Kubernetes, and cloud platforms (AWS/GCP). - Data Analysis: Preprocess data, identify trends, and derive insights using clustering, classification, and anomaly detection techniques. - Collaboration: Work with product managers, engineers, and data scientists to align technical solutions with business goals. What We're Looking For: - Experience: 2+ years in ML, data engineering, or related fields, with a focus on fraud detection or personalization. Technical Skills: - Proficiency in Python, SQL, and big data tools (e.g., Kafka, Spark). - Strong knowledge of ML frameworks (TensorFlow, PyTorch). - Experience with MLOps and cloud technologies (AWS/GCP). - Analytical Skills: Strong understanding of statistical methods and data visualization tools (e.g., Pandas, Matplotlib). - Mindset: Adaptable, innovative, and comfortable in a fast-paced environment