Senior Machine Learning Engineer

We are seeking a skilled and go-getter Sr. Machine Learning Engineer to join our Data Science team and play a pivotal role in driving the model development/production lifecycle. The ideal candidate will collaborate closely with Data Scientists, MLOps, and DataOps teams to implement ML models for assessing transactional risk and fraud, enable automated model retraining, and support robust machine learning inference systems. This role is essential for ensuring efficient, reliable, and scalable workflows to power data-driven insights and machine learning solutions.

What you will do:

• Model Development and Optimization: Design the data-architecture flow for the efficient implementation of real-time model endpoints and/or batch solutions
• Data Exploration and Feature Engineering: Engineer domain-specific features that can enhance model performance and robustness
• Productionization of ML Models: Build pipelines to deploy machine learning models in production with a focus on scalability and efficiency, and participate in and enforce the release management process for models and rules
• Monitoring, Maintenance & Improvement: Implement systems to monitor model performance, endpoints/feature health, and other business metrics; Create model-retraining pipelines to boost performance, based on monitoring metrics; Model recalibration
• Scalable System Design: Design and implement scalable architectures to support real-time/batch solutions; Optimize algorithms and workflows for latency, throughput, and resource efficiency; Ensure systems adhere to company standards for reliability and security
• Innovation and Continuous Improvement: Conduct research and prototypes to explore novel approaches in ML engineering for addressing emerging risk/fraud patterns
• Collaborative Problem Solving: Partner with fraud analysts, risk managers, and product teams to translate business requirements into ML solutions
  

Who you are:

• Bachelor’s or Master’s degree in CS/Engineering/Data-Science or other technical disciplines
• Solid experience in DS/ML engineering
• Proficiency in programming languages such as Python, Scala, or Java
• Hands-on experience in implementing batch and real-time streaming pipelines, using SQL and NoSQL database solutions
• Familiarity with monitoring tools for data pipelines, streaming systems, and model performance
• Experience in AWS cloud services (Sagemaker, EC2, EMR, ECS/EKS, RDS, etc.)
• Experience with CI/CD pipelines, infrastructure-as-code tools (e.g., Terraform, CloudFormation), and MLOps platforms like MLflow
• Experience with Machine Learning modeling, notably tree-based and boosting models supervised learning for imbalanced target scenarios
• Experience with Online Inference, APIs, and services that respond under tight time constraints
• Proficiency in English