5.0 - 10.0 years

5 - 7 Lacs

Remote, , India

On-site

Key Responsibilities: Design and optimize model serving infrastructure with a focus on low latency and cost efficiency Build scalable inference pipelines across different hardware acceleration options Implement monitoring and observability solutions for ML systems Collaborate with ML Engineers to define best practices for deployment Develop enterprise-grade, cost-efficient ML solutions Work closely with MLEs, QA, and DevOps teams in a distributed environment Evaluate new technologies and contribute to system architecture decisions Drive continuous improvements in ML infrastructure Required Experience & Skills: 5+ years of experience in software engineering using Python Hands-on experience with ML frameworks (especially PyTorch) Experience optimizing ML models using hardware accelerators (e.g., AWS Neuron, ONNX, TensorRT) Familiarity with AWS ML services and hardware-accelerated compute (e.g., SageMaker, Inferentia, Trainium) Proven ability to build and maintain serverless architectures on AWS Strong understanding of event-driven patterns (SQS/SNS) and caching strategies Proficiency with Docker and container orchestration tools Solid grasp of RESTful API design and implementation Focus on secure, high-quality code with experience using static code analysis tools Strong problem-solving, algorithmic thinking, and communication skills

Posted 1 month ago

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