AI Engineer

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.
• Feature stores, vector retrieval, RAG, or real-time inference architectures.