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Senior Machine Learning Engineer (Remote - Americas) at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Engineer in the Americas.. As a Senior Machine Learning Engineer, you will play a critical role in building scalable and efficient ML solutions that support risk assessment and fraud detection for high-volume financial transactions. You’ll collaborate closely with Data Scientists, MLOps, and DataOps teams to implement production-grade models, optimize feature engineering, and monitor system performance. This position offers the opportunity to influence end-to-end machine learning workflows, contribute to real-time and batch processing architectures, and explore innovative approaches to emerging financial risks. You will thrive in a fast-paced, remote-first environment that values technical excellence, continuous learning, and cross-functional collaboration.. . Accountabilities. Design, implement, and optimize machine learning models for production, supporting real-time and batch workflows.. Engineer domain-specific features to enhance model robustness and predictive accuracy.. Develop scalable pipelines for deploying, monitoring, and retraining ML models in production.. Build architectures optimized for latency, throughput, and resource efficiency, adhering to reliability and security standards.. Monitor model endpoints, pipeline performance, and key business metrics; implement recalibration and retraining as needed.. Collaborate with fraud analysts, risk managers, and product teams to translate business requirements into ML solutions.. Conduct research and prototypes to explore innovative ML approaches for emerging risk and fraud patterns.. Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical fields.. Strong experience in ML engineering and data science implementation.. Proficiency in Python, Scala, or Java.. Hands-on experience with batch and real-time streaming pipelines using SQL and NoSQL databases.. Familiarity with monitoring tools for pipelines, streaming systems, and model performance.. Experience with AWS cloud services (Sagemaker, EC2, EMR, ECS/EKS, RDS, etc.).. Knowledge of CI/CD pipelines, infrastructure-as-code tools (Terraform, CloudFormation), and MLOps platforms such as MLflow.. Expertise in machine learning modeling, especially tree-based and boosting algorithms for imbalanced targets.. Experience with online inference systems, APIs, and latency-sensitive services.. Strong proficiency in English.. Preferred/Plus:. ML applications in financial decision-making (credit risk, fraud prevention).. AWS Sagemaker or similar DS/ML workbench experience.. Containerization and orchestration (Docker, Kubernetes).. Feature store development and integration.. Distributed data systems (Kafka, Spark, Hadoop) and workflow orchestration tools (e.g., Airflow).. Company Location: Mexico.