Machine Learning Engineer (Consultant)

This is a remote position. 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

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 Apply To This Job