Location:
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
Details