Senior Machine Learning System Builder

Own detection capabilities end to end, from problem framing and data through experimentation to models running in production and telemetry. Build and maintain data pipelines for sample sourcing, ground truth labeling, feature extraction, and versioned datasets. Design, train, fine-tune, and evaluate models, deploying versioned artifacts like ONNX to JVM-based backends with documented performance benchmarks. Establish automated model build and release pipelines using SageMaker Pipelines for reproducible training and automated evaluation gates. Monitor model quality in production via telemetry feedback loops, false positive escalations, and drift monitoring to manage retraining cadence. Design evaluation methodology for adversarial domains using time-split validation and strict false positive ceilings. Automate the ML lifecycle with AI-assisted development for labeling assistance, evaluation runs, and reporting. Set your own experimentation roadmap prioritized by measurable customer-facing detection gains and share learnings across the team.