This is a remote position.

Responsibilities: â Optimize ML model serving for low-latency inference (target: sub-200ms P95) on EKS â Advise on and implement AWS-native ML infrastructure (SageMaker endpoints, model registry, A/B testing, monitoring) â Support ML-optimized rule weight calibration — training logistic regression / LightGBM on rule-fi re indicators to learn optimal rule weights from labeled data â Assist with model retraining pipeline automation and drift detection â Contribute to model explainability documentation (SHAP-based attribution) for regulatory compliance â Participate in model governance: version control, audit trails, threshold confi guration per participating institution â Support load testing and performance benchmarking of the ML scoring pipeline â Provide input for the technical proposal and architecture documentation

Requirements

Requirements: â AWS Machine Learning Specialty Certification (or AWS Certifi ed Machine Learning Engineer – Associate) — current and valid â 3+ years of hands-on experience deploying ML models in production on AWS â Strong Python skills (scikit-learn, LightGBM/XGBoost, pandas) â Experience with containerized ML serving (Docker, Kubernetes/EKS) â Familiarity with model monitoring, drift detection, and retraining pipelines Preferred Qualifications â Experience in fraud detection, AML, or fi nancial risk systems â Familiarity with graph-based ML (GNN, NetworkX) for network analysis â Experience with Apache Kafka or Apache Flink for streaming ML â Knowledge of SHAP or other model explainability frameworks â Experience with SageMaker (endpoints, model registry, pipelines)

Benefits

â Fully Remote â Flexible working hours (part-time, ~15–20 hours/week) â Potential to extend engagement based on project phase progression

Machine Learning Engineer (Consultant)

Key skills

About this role

Requirements

Benefits