Senior Machine Learning Engineer

About the Role:

As a Senior Machine Learning Engineer, you will play a vital role in designing, building, testing, and delivering advanced Machine Learning (ML) models and software components aimed at tackling significant business challenges. Collaborating with various teams—including Business, Product, Architecture, Engineering, and Data Science—you will analyze large datasets (both structured and unstructured) to unveil opportunities in ML and Artificial Intelligence (AI). Your work will make a meaningful impact across our organization.

Key Responsibilities:

• Transform and enhance data science prototypes into production-ready ML and AI systems using suitable datasets.
• Research and implement cutting-edge ML algorithms and tools to design new systems powered by ML and AI.
• Create and manage end-to-end ML workflows and analysis tools that streamline model development at scale.
• Develop intelligent AI systems that perform complex tasks, interact with enterprise data, and support human decision-making.
• Extend existing ML libraries and frameworks to align with advancements in Data Science and ML for enterprise utilization.
• Architect and maintain scalable cloud solutions to facilitate prediction model transactions.
• Conduct experiments to evaluate ML models and AI agents, focusing on performance and business impact.
• Implement monitoring and observability for ML systems to ensure peak performance and continuous improvement.
• Collaborate with a talented team of Designers, Architects, Software Engineers, Data Scientists, and Data Engineers to deliver innovative ML products.

Qualifications:

• Experience designing and building solutions in data-intensive, distributed computing environments.
• Proficient in ML and AI methodologies, familiar with frameworks like Keras and PyTorch, and tools such as scikit-learn.
• Strong background in implementing scalable solutions for both batch and live streaming predictions.
• Expertise in developing ML models, including classification and regression, NLP, and deep learning focused on operationalizing these models.
• Experience with AI systems, task orchestration, and enterprise system integration.
• Knowledge in deploying software that supports high transaction volumes and numerous users.
• Strong grasp of statistics and advanced mathematics, applicable to evaluating ML models.
• Excellent collaborative skills in technical and cross-functional environments.
• Outstanding written and verbal communication abilities.
• Bachelor's degree in a quantitative discipline (e.g., mathematics, computer science, engineering) or equivalent experience.

Basic Qualifications:

U.S. Citizenship or legal authorization to work in the U.S. is required.