AWS Lambda, Financial Services, Machine Learning, Model Validation, Python Programming/Scripting Language, SQL (Structured Query Language), ServiceNow, Topology, Training Data Sets
LOCATION
Jersey City, NJ
POSTED
9 days ago
Duration: 6+ months Location: Onsite in Jersey City, NJ
Role Impact:
This role owns the intelligence layer for a large-scale incident classification platform within a financial services environment. The Machine Learning Engineer will design and operationalize a multi-tier classification engine combining rules-based logic, Extreme Gradient Boosting (XGBoost), and large language model (LLM) agent workflows.
Requirements:
Python
XGBoost
MLflow
Kafka Streaming
SQL
Key Responsibilities:
Design the three-tier classification engine using rules-based logic, XGBoost, and LLM agent orchestration.
Build feature engineering pipelines for temporal, topological, and semantic scoring models.
Train and validate XGBoost models using historical ServiceNow incident datasets.
Implement SHAP for classification transparency and audit readiness.
Develop automated monthly retraining pipelines using AWS Lambda and MLflow.
Implement model drift detection frameworks using Evidently.