
Example Responsibilities:
- Build and optimize model serving infrastructure with a focus on inference latency and cost optimization
- Architect efficient inference pipelines that balance latency, throughput, and cost across various acceleration options
- Develop monitoring and observability solutions for ML systems
- Collaborate with ML Engineers to establish best practices for optimized model deployment
- Implement cost-efficient, enterprise-scale solutions
- Collaborate in a cross-functional, distributed team for continuous system improvement
- Work with MLEs, QA Engineers, and DevOps Engineers
- Evaluate and implement new technologies and tools
- Contribute to architectural decisions for distributed ML systems
Experience and Qualifications:
- 5+ years of experience in software engineering with Python
- Experience with ML frameworks, particularly PyTorch
- Experience 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 architectures
- Deep understanding of event-driven processing patterns, SQS/SNS and serverless caching solutions
- Experience with containerization using Docker and orchestration tools
- Strong knowledge of RESTful API design and implementation
- Proficiency in writing good quality & secure code and be familiar with static code analysis tools
- Excellent analytical, conceptual and communication skills in spoken and written English
- Experience 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 systems
- Experience working in regulated industries with strict compliance requirements for cloud-native solutions

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Position Overview
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