Senior Machine Learning Engineer

Largeton Group • Contract • Chicago, IL, US • 4m ago

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.

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