Machine Learning Engineer jobs in Chicago, IL

The role involves developing generalizable tools and libraries to support machine learning projects and facilitating code reuse across the team. You will collaborate with data scientists to transition models from prototype to production while maintaining high standards for code quality and documentation.

The role involves advancing AI solutions from proof of concept to full production readiness while ensuring scalability and robustness. You will write high-quality code, implement testing protocols, and collaborate with stakeholders to align technical outputs with business needs.

You will own the platform for taking fraud, risk, and payment models from notebook to production, including real-time inference and feature pipelines. You will also be responsible for model deployment, monitoring, drift detection, and integrating third-party risk vendors.

Define and implement agentic AI architectures and policy-as-code standards to optimize cloud financial management across multi-cloud environments. Lead delivery squads in executing FinOps automations, forecasting, and executive reporting to drive measurable business value.

You will architect and build transformative generative AI and multi-agent systems to redefine financial analysis and credit decision-making. The role involves leading technical strategy, mentoring engineering teams, and establishing ML governance standards across the organization.

Drive the architectural vision and system design for core AI/ML-based services while leading complex, multi-quarter initiatives. Mentor senior and mid-level data scientists and engineers while establishing standard methodologies and code quality expectations.

The Senior Machine Learning Engineer designs and delivers scalable, production-grade machine learning and operations research solutions to enhance data-driven decision-making. This role also leads the development of automated data pipelines and creates reusable frameworks to accelerate model delivery across the enterprise.

You will own the technical vision and production quality for the Predictive and Generative AI platform, ensuring ML systems are fast, safe, and efficient at scale. You will drive complex infrastructure initiatives, mentor senior engineers, and collaborate across teams to deliver personalized customer experiences.

The role involves designing scalable machine learning infrastructure, implementing CI/CD pipelines for model deployment, and monitoring models in production, alongside developing Generative AI solutions. Responsibilities also include preprocessing and analyzing datasets, developing and fine-tuning ML models, and communicating results to stakeholders.

The Senior Machine Learning Engineer will own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to production deployment. They will also collaborate with cross-functional teams to enhance fraud detection effectiveness and scale infrastructure.

You will own the production ML systems and build out the MLOps platform infrastructure to support a suite of B2C financial services. This involves designing, deploying, and operating scalable pipelines, model-serving infrastructure, and automated monitoring tools.

Develop and deploy AI and machine learning models to solve complex business problems while collaborating with cross-functional teams. You will perform data preprocessing, feature engineering, and model optimization to ensure scalability and business impact.