Senior Software Development Engineer

Location Details: Colombia, remote At GoDaddy, the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) , and some work entirely remotely.​ This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings. Join Our Team The Commerce Data & ML team is at the core of powering intelligent and secure experiences for GoDaddy’s Commerce division. Partnering with the Data Science team, we support a wide range of machine learning and data engineering efforts that help GoDaddy Commerce understand its customers, prevent fraud, and tailor product experiences. We work closely with the Risk team to build and maintain production ML Risk Models, such as Transaction Risk Scoring and Merchant Risk Models. We also collaborate with the Growth team to enable data-driven marketing and customer segmentation through reliable pipelines and predictive models. We’ve evolved a robust ML platform built on SageMaker Pipelines, Tecton feature store, Glue, GitHub Actions, and MLflow - with support for both batch and real-time inference. Also, recently, we’ve begun developing prompt engineering workflows for LLM-powered chatbots. As a Senior Machine Learning Engineer on the Commerce Data & ML team in Bulgaria, you will play a key role in building production-grade pipelines and scalable ML systems that support fraud mitigation, customer insights, and AI-driven experiences. What you'll get to do... • Design, build, and maintain robust ML pipelines for both batch and real-time inference using tools such as SageMaker Pipelines, EventBridge, MLflow, and feature stores. • Collaborate with Data Scientists, Engineers, and stakeholders to implement, monitor, and optimize ML workflows—including model training, evaluation, drift detection, and deployment. • Enhance and manage CI/CD pipelines for ML models, oversee model promotion and retraining workflows, and ensure seamless integration across different environments. • Lead the end-to-end delivery of ML features from architecture and implementation to monitoring and iteration, while guiding and mentoring junior engineers and promoting best practices. • Support operational excellence by participating in on-call rotations, production incident response, post-mortems, and contributing to the planning and scoping of ML initiatives. Your experience should include... • 5+ years in the software industry, with at least 3+ years focused on ML engineering, pipelines, or applied machine learning. • Hands-on experience with AWS SageMaker Studio, Tecton, and proficiency in Python and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow). • Experience with experiment tracking (MLflow), orchestrating ML workflows (SageMaker Pipelines, EventBridge), and building/monitoring both batch and real-time inference pipelines. • Familiarity with data preparation and streaming tools (Glue, EMR, Flink) for feature pipelines. • Solid understanding of CI/CD for ML (model deployment, endpoint configuration, validation) and strong cross-functional collaboration skills with Data Science, Risk, and Engineering teams. You might also have... • Experience building dual-model pipelines (e.g., NeuralNet + XGBoost) and evaluating ensemble performance. • Knowledge of business-aligned success metrics like recall, hit rate, false positives, and model drift thresholds. • Experience with fraud detection, risk modeling, or financial domains. • Familiarity with prompt engineering, LLM integration, and evaluation workflows for generative AI. • Backend or data engineering skills in Java, Scala, or Go (nice to have). We encoura