Description

You will design, develop, and implement end-to-end machine learning systems and large-scale data pipelines. You own the release pipeline from data pre-processing to model deployment.

Responsibilities

• Design and deploy end-to-end ML systems, covering data pre-processing, feature engineering, model training, and evaluation.
• Build and own cloud-native APIs to deploy ML models for internal stakeholders.
• Develop and maintain both real-time and batch ML pipelines.
• Collaborate with data scientists and software engineers to design large-scale batch and real-time ML services.
• Automate campaigns to optimize compute and infrastructure costs.

Required Skills

• 2+ years of experience in machine learning, data science, or a related field.
• Advanced proficiency in Python, Scala, and SQL.
• Hands-on experience with PySpark and Hadoop.
• Strong knowledge of machine learning algorithms, statistical models, and feature engineering.
• Experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
• Practical experience with cloud environments like AWS or Azure for data handling.
• Bachelor's or Master's degree in a quantitative field or computational discipline from a Tier 1 or 2 college.
• Ability to communicate technical concepts effectively to non-technical stakeholders.

Preferred Skills

• Experience with Databricks, H2O Driverless AI, or MLOps environments.
• Experience working within the financial industry.

Key Skills

Education

Any Graduate