Senior Machine Learning Engineer

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💎 Seniority level: Senior, Solid experience in DS/ML engineering

📍 Location: Brazil, the U.S., and Canada

🔍 Industry: Payments

🗣️ Languages: English

⏳ Experience: Solid experience in DS/ML engineering

🪄 Skills: AWSBackend DevelopmentDockerPythonSQLAmazon RDSAWS EKSFrontend DevelopmentJavaKafkaKubernetesMachine LearningMLFlowAirflowAlgorithmsData engineeringData scienceREST APINosqlPandasSparkCI/CDTerraformScalaData modelingEnglish communication

Requirements:

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.

Responsibilities:

Design the data-architecture flow for the efficient implementation of real-time model endpoints and/or batch solutions.
Engineer domain-specific features that can enhance model performance and robustness.
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.
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.
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.
Conduct research and prototypes to explore novel approaches in ML engineering for addressing emerging risk/fraud patterns.
Partner with fraud analysts, risk managers, and product teams to translate business requirements into ML solutions.

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