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.

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