Senior Machine Learning Engineer

Role Description

We are looking for an experienced and driven Machine Learning Engineer to join our Advanced Analytics team. You will play a pivotal role in advancing our machine learning capabilities, focusing on the building, training, deploying, scoring, and monitoring of models for various use cases, including personalized recommenders, forecasting, and LLM modeling.

Responsibilities

• Model Development & Deployment: Develop and deploy Machine Learning, Deep Learning, and GenAI models to enhance operational efficiency and customer experience.
• Continuous Improvement: Improve models by monitoring performance, conducting A/B testing, and implementing feedback loops.
• Architecture & Scalability: Architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing and distributed workloads to support scalable pipelines.
• Cross-functional Collaboration: Work with engineers and product managers to integrate AI solutions into production systems within a fast-paced Agile environment.
• Operational Excellence: Own production ML systems, participating in on-call rotations, troubleshooting incidents, and maintaining overall model reliability.
• Innovation: Stay updated with the latest advancements in AI to ensure our solutions remain cutting-edge.

Qualifications

• Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
• 3+ years of experience designing, building, and operating production-scale machine learning systems.
• Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) for large-scale datasets.
• Strong experience with Scikit-Learn, MLlib, and PyTorch, including regression, time series, clustering, and deep learning.
• Deep understanding of Spark execution models, partitioning strategies, shuffle optimization, and vectorized processing (Pandas UDFs).
• Strong SQL expertise and experience deploying ML workloads to AWS (EC2, S3, DynamoDB, Lambda).
• Experience implementing MLOps / LLMOps practices.

Key Competencies

• Ownership: Ability to manage strategic initiatives in a rapidly evolving QSR environment.
• Adaptability: Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude.
• Outcome-Oriented: Focus on analyzing and visualizing data to drive continuous improvement across the business.
• Professionalism: Maintain transparency and professionalism in communication within the team and with stakeholders.

Benefits

• Flexible / Remote options available.