Machine Learning Engineer (Consultant)

Category: Technology

Location:

• 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
  

• 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
  

Originally posted on Himalayas