Roles and Responsibilities

• Develop strategies for prompt engineering and management to enhance LLM outputs and ensure consistency and safety.
• Continuously optimize LLMOps processes and infrastructure for cost-efficiency while maintaining high performance and reliability.
• Apply techniques like quantization, distillation, and pruning to optimize LLM models for efficient inference on AWS infrastructure.
• Design and implement MLOps infrastructure on AWS tailored for LLMs, leveraging services like SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and more.
• Build and manage CI/CD pipelines specifically for LLM deployment, addressing unique challenges like model size, inference optimization, and versioning.
• Work closely with data scientists, researchers, and software engineers to integrate LLM models into production systems effectively.

Required Experience/Skills

• Strong proficiency in AWS services relevant to MLOps and LLMs, including SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and API Gateway.
• Deep understanding of LLM architectures (e.g., Transformers), training techniques, and inference optimization strategies.
• Proficiency in Python and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation), REST API frameworks (e.g., Flask, FastAPI), and LLM libraries (e.g., Hugging Face Transformers).
• Familiarity with monitoring and logging tools for LLMs, such as Prometheus, Grafana, and CloudWatch.
• Experience with Docker and container orchestration (e.g., Kubernetes, ECS) for LLM deployment.

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