Machine Learning Engineer

Responsibilities:

● Optimize ML model serving for low-latency inference (target: sub-200ms P95) on EKS

● Advise on and implement AWS-native ML infrastructure (SageMaker endpoints, model registry, A/B testing, monitoring)

● Support ML-optimized rule weight calibration — training logistic regression / LightGBM on rule-fi re indicators to learn optimal rule weights from labeled data

● Assist with model retraining pipeline automation and drift detection

● Contribute to model explainability documentation (SHAP-based attribution) for regulatory compliance

● Participate in model governance: version control, audit trails, threshold confi guration per participating institution

● Support load testing and performance benchmarking of the ML scoring pipeline

● Provide input for the technical proposal and architecture documentation

Requirements:

● AWS Machine Learning Specialty Certification (or AWS Certifi ed Machine Learning Engineer – Associate) — current and valid

● 3+ years of hands-on experience deploying ML models in production on AWS

● Strong Python skills (scikit-learn, LightGBM/XGBoost, pandas)

● Experience with containerized ML serving (Docker, Kubernetes/EKS)

● Familiarity with model monitoring, drift detection, and retraining pipelines

Preferred Qualifications

● Experience in fraud detection, AML, or fi nancial risk systems

● Familiarity with graph-based ML (GNN, NetworkX) for network analysis

● Experience with Apache Kafka or Apache Flink for streaming ML

● Knowledge of SHAP or other model explainability frameworks

● Experience with SageMaker (endpoints, model registry, pipelines)

Benefits:

● Fully Remote

● Flexible working hours (part-time, 15–20 hours/week)

● Potential to extend engagement based on project phase progression