• Design and build scalable ML services for enrichment workflows, including model training pipelines and high-performance inference APIs • Deploy and optimize models using modern inference libraries and frameworks (ONNX, vLLM, TensorRT, etc.) to achieve low-latency, high-throughput performance • Collaborate with software engineers and product teams to define data requirements, feature engineering strategies, and model evaluation metrics • Build robust monitoring, observability, and evaluation systems to ensure model quality and service reliability in production • Stay current with emerging ML techniques, tools, and best practices, particularly in areas like model optimization, efficient inference, and large-scale data processing