AWS

AWS is used as the primary cloud foundation for building and operating production-grade platforms, supporting application delivery, data pipelines, and machine learning systems.

Rather than treating AWS as a collection of isolated services, it is approached as a composable platform, where infrastructure, networking, security, and runtime concerns are intentionally designed and governed.

Key Capabilities

• Compute & Runtime Foundations
  Supports containerized, virtualized, and serverless workloads using EKS, EC2, and Lambda, allowing teams to choose the right execution model per use case.

• Data & Storage Backbone
  Provides durable, scalable storage patterns using S3, EBS, and EFS for datasets, artifacts, and stateful workloads.

• Managed Databases & Caching
  Enables reliable relational storage and low-latency access through RDS and ElastiCache.

• Networking & Traffic Control
  Offers isolated, secure networking with VPCs, load balancers, DNS, and edge distribution.

• LLM & Agentic Enablement (Bedrock)
  Hosts foundation models used within agentic workflows, enabling LLM-powered systems without operational overhead of model hosting.

Experience & Platform Contribution

Designed and operated AWS-based infrastructure as part of a shared platform, enabling application teams to deliver services, ML workloads, and LLM-powered systems within clearly defined guardrails.

Key contributions included:

• Establishing secure and repeatable infrastructure patterns across environments
• Enabling agentic LLM workflows using AWS Bedrock, including chat, insight, and report agents
• Implementing RAG-based architectures to ground LLM outputs in domain data
• Balancing platform standards with team autonomy through opinionated defaults
• Advising teams on cloud architecture, security boundaries, and operational trade-offs

AWS formed the underlying platform layer that supported both traditional application workloads and modern ML/LLM systems, with emphasis on reliability, security, and long-term maintainability.