Job Title

Responsibilities: Build and optimize model serving infrastructure with a focus on inference latency and cost optimizationArchitect efficient inference pipelines that balance latency, throughput, and cost across various acceleration optionsDevelop monitoring and observability solutions for ML systemsCollaborate with ML Engineers to establish best practices for optimized model deploymentImplement cost-efficient, enterprise-scale solutionsCollaborate in a cross-functional, distributed team for continuous system improvementWork with MLEs, QA Engineers, and DevOps EngineersEvaluate and implement new technologies and toolsContribute to architectural decisions for distributed ML systems Experience and Qualifications:5+ years of experience in software engineering with PythonExperience with ML frameworks, particularly PyTorchExperience optimizing ML models with hardware acceleration (AWS Neuron , ONNX, TensorRT)Experience with AWS ML services and hardware-accelerated instances (Sagemaker, Inferentia, Trainium)Proven experience building and operating AWS serverless architecturesDeep understanding of event-driven processing patterns, SQS/SNS and serverless caching solutionsExperience with containerization using Docker and orchestration toolsStrong knowledge of RESTful API design and implementationProficiency in writing good quality & secure code and be familiar with static code analysis toolsExcellent analytical, conceptual and communication skills in spoken and written EnglishExperience applying Computer Science fundamentals in algorithm design, problem solving, and complexity analysis Great to have Experience and Qualifications: Experience with any of the following: model compilation and quantization, performance profiling and benchmarking ML inference systemsExperience working in regulated industries with strict compliance requirements for cloud-native solutions