Roles and 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
Solid 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