Overview
The Senior ML Platform / MLOps Engineer will play a critical role in supporting the real-time machine learning infrastructure for fraud detection. This remote position focuses on enhancing low-latency and highly reliable production systems and involves collaboration with engineering teams to ensure efficient ML platform operations. The role demands a hands-on approach to engineering, with an emphasis on independent ownership of workstreams in high-stakes environments.
Responsibilities
- Develop and maintain ML platform and feature-store infrastructure.
- Implement low-latency, production-level systems using Kafka and Spark Structured Streaming.
- Build model training, serving, and deployment pipelines.
- Utilize AWS and Terraform for infrastructure management.
- Work with Databricks, PySpark, Delta Lake, and MLflow for data processing and ML workflows.
- Establish CI/CD processes and enhance system observability through monitoring.
- Conduct canary and shadow deployments to ensure reliability and performance.
- Adopt best practices in MLOps and maintain compliance with latency and cost constraints.
Requirements
- Proven experience in ML platform engineering and MLOps in production environments.
- Strong proficiency in Python and AWS services.
- Experience with Kafka, Spark Structured Streaming, and building data pipelines.
- Familiarity with CI/CD processes and observability tools.
- Knowledge of Databricks, PySpark, Delta Lake, and MLflow.
- Understanding of DynamoDB, Redis, and feature stores is a plus.
- Experience in consulting roles and the ability to work independently.
- Background in payments, fraud detection, or low-latency environments is highly desirable.