Machine Learning Engineer (Consultant)

Role Description

• 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-fire 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 configuration per participating institution
• Support load testing and performance benchmarking of the ML scoring pipeline
• Provide input for the technical proposal and architecture documentation

Qualifications

• AWS Machine Learning Specialty Certification (or AWS Certified 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

Requirements

• Experience in fraud detection, AML, or financial 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