Machine Learning Engineer

Join a high-impact Machine Learning Engineering team supporting critical decisioning platforms across a leading financial services organization. This team develops and scales production machine learning systems that power credit decisioning, fraud detection, risk assessment, and partner-facing applications., As a Sr Machine Learning Engineer, you will work at the intersection of software engineering, cloud infrastructure, and machine learning. Partnering closely with Data Scientists, Product Managers, and Engineering teams, you will design, deploy, and scale machine learning solutions that deliver measurable business impact. This role is ideal for engineers who enjoy building cloud-native ML platforms, operationalizing models, and driving production excellence at enterprise scale.

• Design, develop, and deploy production-grade machine learning solutions on AWS
• Build and maintain scalable ML pipelines for model training, validation, deployment, and monitoring
• Partner with Data Scientists to operationalize advanced analytical and machine learning models
• Develop cloud-native infrastructure to support machine learning workloads
• Optimize model performance, reliability, and operational efficiency
• Implement best practices for testing, CI/CD, governance, and monitoring across the ML lifecycle
• Support enterprise-scale machine learning initiatives across:

• Credit Decisioning
• Fraud Detection
• Risk Assessment
• Partner and Acquisition Programs

• Contribute to the evolution of ML platform capabilities and engineering standards

Python (Spark, Pandas, NumPy)

• AWS

• ML Ops / ML tooling experience

Nice to haves:

• AWS Cert

• Kubernetes, * 5+ years of experience in Machine Learning Engineering, Software Engineering, or related disciplines

• Strong proficiency in Python
• Deep expertise with AWS services, including ECS, EC2, EKS, S3, and cloud-native architectures
• Experience designing and deploying machine learning applications in production environments
• Strong understanding of MLOps principles and the machine learning lifecycle
• Experience with distributed data processing frameworks such as Apache Spark
• Strong software engineering fundamentals, including version control, testing, and CI/CD practices

Technical Environment:

Cloud & Infrastructure

• AWS (EC2, ECS, EKS, S3)
• Kubernetes
• Docker
• Cloud-Native Architecture

Machine Learning & MLOps

• Kubeflow
• Apache Airflow
• Model Training & Deployment
• ML Pipeline Orchestration
• CI/CD Automation
• Model Monitoring & Governance

Programming & Data Technologies

• Python
• Apache Spark
• SQL
• Pandas
• NumPy
• Databricks