Job Summary – Senior Machine Learning Engineer (Chicago, IL) • Lead the design and implementation of end-to-end machine learning (ML) solutions covering data ingestion, ML processing, and results delivery/activation for algorithmic products. • Collaborate cross-functionally with data science, data engineering, and data architecture teams to architect, prototype, and productionize ML workflows. • Act as both a solutions architect and hands-on implementation engineer, guiding teams toward best-in-class algorithmic product implementations. • Develop and optimize ML models, including deep learning architectures, LLMs, and BERT-based classifiers for personalization, generative AI, forecasting, and decision science domains. • Implement distributed training workflows (e.g., PyTorch) and optimize models for hardware acceleration (GPU, TPU, AWS Inferentia/Trainium). • Design and build scalable ML infrastructure and AI services for both real-time streaming and offline batch use cases using AWS cloud services. • Enhance and maintain MLOps platforms, including Feature Store, ML Observability, ML Governance, and automated CI/CD pipelines. • Implement data processing workflows and ensure high-quality data ingestion, cleansing, and feature engineering. • Build and deploy models across cloud compute environments (EC2, EKS, SageMaker, etc.), leveraging infrastructure-as-code. • Monitor, profile, and optimize ML system performance for accuracy, latency, and cost efficiency. • Stay updated on the latest ML engineering patterns, AWS advancements, and best practices in cloud-based ML deployment. • Ensure all solutions meet data governance, security, and architectural standards in partnership with relevant teams. • Provide technical leadership, mentorship, and guidance on modeling and infrastructure to peers and partners. Required Qualifications • Minimum 13+ years of experience in software/ML engineering (5+ years in cloud-based ML product solutions). • Master’s degree in Computer Science, Software Engineering, or related field. • Expertise in AWS, Python, SQL, PySpark, Docker. • Experience with MLOps, CI/CD, Agile methodology. • Strong communication and teamwork skills. • Experience in building ML systems at scale for both streaming and batch architectures.