**Roles and Responsibilities :

Build and optimize model serving infrastructure with a focus on inference latency and cost optimization

Architect productive 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**

📌 MLOps Engineer (SDE 3) (Bengaluru)
🏢 Talentoj
📍 Bengaluru