M
AI Engineer
Madison-Davis, LLC
3 hours ago
Full-time
On-site
A large financial institution is seeking a senior Data Science and Analytics Engineer to help expand and operationalize enterprise AI and machine-learning capabilities.
Please double check you have the right level of experience and qualifications by reading the full overview of this opportunity below.
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.
Please double check you have the right level of experience and qualifications by reading the full overview of this opportunity below.
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.