M
AI Engineer
Madison-Davis, LLC
2 hours ago
Full-time
On-site
Mundelein, Illinois, United States
Job Description
All the relevant skills, qualifications and experience that a successful applicant will need are listed in the following description. A large financial institution is seeking a senior Data Science and Analytics Engineer to help expand and operationalize enterprise AI and machine-learning capabilities. This is a highly hands-on position for someone who combines deep technical expertise with the ability to provide direction, establish standards, and communicate effectively with business and technology leadership. You will build production-grade AI and analytics solutions while helping mature the organization's engineering, MLOps, governance, and deployment practices. Responsibilities Design, develop, and deploy enterprise machine-learning, AI, and advanced-analytics solutions. Own analytics delivery from problem definition and feature engineering through model development, deployment, monitoring, and adoption. Build scalable ML pipelines using Python, Databricks, Spark, and cloud-native technologies. Integrate models into enterprise applications and operational workflows. Establish MLOps standards for deployment, CI/CD, experiment tracking, model monitoring, and lifecycle management. Support model explainability, validation, governance, and auditability. Partner with business, engineering, risk, compliance, data, and operations teams. Provide technical leadership while remaining directly involved in development and implementation. Role Requirements 10+ years of experience across data science, AI/ML engineering, advanced analytics, quantitative modeling, or related areas. Deep hands-on Python and SQL experience. Strong machine-learning, predictive-modeling, and statistical-analytics background. Demonstrated experience delivering ML or AI solutions into production. Databricks, Spark, and cloud-analytics experience. Experience with modern ML frameworks and model-lifecycle tooling. Practical MLOps experience including deployment, CI/CD, monitoring, and experiment tracking. Experience within financial services or another regulated enterprise environment. Strong communication and stakeholder-management skills. Ability to provide technical leadership without moving away from hands-on execution. Nice to Have Banking, lending, payments, fraud, AML, risk, or regulatory-analytics experience. Production GenAI, LLM, NLP, or intelligent-automation experience. Azure ML and MLflow. Model-risk or AI-governance experience. xsgimln Feature stores, vector retrieval, RAG, or real-time inference architectures.
All the relevant skills, qualifications and experience that a successful applicant will need are listed in the following description. A large financial institution is seeking a senior Data Science and Analytics Engineer to help expand and operationalize enterprise AI and machine-learning capabilities. This is a highly hands-on position for someone who combines deep technical expertise with the ability to provide direction, establish standards, and communicate effectively with business and technology leadership. You will build production-grade AI and analytics solutions while helping mature the organization's engineering, MLOps, governance, and deployment practices. Responsibilities Design, develop, and deploy enterprise machine-learning, AI, and advanced-analytics solutions. Own analytics delivery from problem definition and feature engineering through model development, deployment, monitoring, and adoption. Build scalable ML pipelines using Python, Databricks, Spark, and cloud-native technologies. Integrate models into enterprise applications and operational workflows. Establish MLOps standards for deployment, CI/CD, experiment tracking, model monitoring, and lifecycle management. Support model explainability, validation, governance, and auditability. Partner with business, engineering, risk, compliance, data, and operations teams. Provide technical leadership while remaining directly involved in development and implementation. Role Requirements 10+ years of experience across data science, AI/ML engineering, advanced analytics, quantitative modeling, or related areas. Deep hands-on Python and SQL experience. Strong machine-learning, predictive-modeling, and statistical-analytics background. Demonstrated experience delivering ML or AI solutions into production. Databricks, Spark, and cloud-analytics experience. Experience with modern ML frameworks and model-lifecycle tooling. Practical MLOps experience including deployment, CI/CD, monitoring, and experiment tracking. Experience within financial services or another regulated enterprise environment. Strong communication and stakeholder-management skills. Ability to provide technical leadership without moving away from hands-on execution. Nice to Have Banking, lending, payments, fraud, AML, risk, or regulatory-analytics experience. Production GenAI, LLM, NLP, or intelligent-automation experience. Azure ML and MLflow. Model-risk or AI-governance experience. xsgimln Feature stores, vector retrieval, RAG, or real-time inference architectures.