Machine Learning Engineer

Role - MLOps

Location - Remote

Experience - 5 + Yrs

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