Senior Machine Learning Engineer

Requirements

• You have extensive experience developing, deploying, and maintaining machine learning models in production environments.
• You're proficient in Python and experienced with ML frameworks such as TensorFlow, PyTorch, or similar.
• You possess a strong background in data processing and analysis, using tools like Pandas, NumPy, Apache Spark or similar.
• You have experience with orchestration technologies such as Airflow, Kubeflow, Flyte or similar.
• You're well-versed in AWS cloud and have hands-on experience with containerization and orchestration tools (i.e., Docker, Kubernetes).
• You have a solid understanding of core ML concepts, including model evaluation, monitoring, and optimization.
• You thrive in agile, collaborative settings and communicate complex technical concepts with clarity.
• Experience with MLOps best-practices and infrastructure-as-code (such as Terraform) are a plus.
• Experience with AWS Sagemaker is a plus.
• Experience deploying and running models in low-latency production environments is a plus.

Benefits

Additional Information

About Payrails Payrails is a global payment software company helping leading enterprises to take control of their payment operations and maximize performance. With deep experience in the payments space and firsthand knowledge of merchants' challenges, we've seen how fragmented, complex, and inefficient these systems can become. Now, we are setting a new industry standard for how enterprises around the world manage and optimize payments, with more control, visibility and flexibility than ever before. Our vision is to reimagine payments from the ground up. We are building a deeply integrated meta layer that spans the entire payment lifecycle with a modular architecture. This helps us give leading brands building blocks to craft solutions to most complex operational challenges, tailor seamless customer experiences and grow their business. We are backed by some of the world's top investors including Andreessen Horowitz , HV Capital , EQT Ventures and General Catalyst , who share our mission to simplify payment complexity globally. At Payrails, we're committed to building a team of exceptional people - not just talented, but driven to build, solve, and deliver at the highest level. Excellence isn't just a value here; it's a way of working. We believe that great people thrive in environments where there's trust, clarity, and shared purpose. We work openly and collaboratively, with deep respect for each other's craft. Everyone is encouraged to understand the bigger picture - what we're building, why it matters, and what stands in the way. When people have that context, they move faster, take ownership, and make better decisions. We care about creating a culture where people feel inspired to do their best work and a deep sense of responsibility to help us bring our vision to life. Success at Payrails means staying focused on the problems that matter most, and executing with purpose. Your team At Payrails, data is at the core of everything we do. We don't just analyze data; we turn it into actionable insights that fuel smarter decision-making and drive the future of our products. As we continue to revolutionize the payments industry we are seeking an exceptional Senior Data Engineer to help us in our mission. Our Data Team is at the forefront of driving transformation, empowering every department to unlock the full potential of data How you will make an impact Design, build, test, and deploy robust machine learning models that power critical business decisions. Develop and maintain scalable pipelines to support both model training and real-time inference. Collaborate with data scientists, software engineers, and product teams to transform innovative ideas into production-grade solutions. Implement industry best practices to ensure model reliability, scalability, and continuous performance improvement. Stay abreast of the latest advancements in machine learning and AI, and integrate new techniques to keep our solutions at the cutting edge.

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