SDE III MLOps

Role Purpose:

As a Software Development Engineer in the MLOps team, you will help design and build scalable, high-performance infrastructure for deploying and serving machine learning models.

Role Value:

As an SDE III, youll architect robust ML infrastructure to support efficient model deployment, serving, and optimization. You'll develop CI/CD pipelines for ML workflows, utilize compiler and hardware-based optimizations to improve inference performance, and drive cost efficiency. You will also influence development processes and tooling for continuous improvement.

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
  

Nice to Have:

• Experience with model compilation, quantization, and inference benchmarking
  
• Exposure to regulated environments with compliance-heavy requirements for cloud-based solutions

  Role & responsibilities

Preferred candidate profile