I'm Ashwin Giridharan, a Cloud & Enterprise Solutions Architect focused on scalable technology, Generative AI, LLMs, RAG, Agentic AI, and practical AI architecture.
I work at the intersection of enterprise architecture, cloud platforms, distributed systems, and emerging AI technologies.
My current focus is understanding how LLMs, Retrieval-Augmented Generation, AI agents, and intelligent workflows can move from experimentation into reliable, production-ready systems.
A practical series exploring AI from fundamentals to production architecture. I share what I'm learning, the architecture behind it, and where these technologies can actually be useful.
How large language models work and where they fit in modern applications.
When to use a model as-is, when to retrieve knowledge, and when to specialize behavior.
From embeddings and retrieval to hybrid and production-ready knowledge systems.
Exploring agents, tools, orchestration, memory, planning, and intelligent workflows.
Evaluation, observability, reliability, security, cost, and operating AI at scale.
Exploring an AI-powered approach to cloud and Kubernetes analysis, including intelligent log analysis, orchestration, and production AI workflows.
View GitHub →Designing scalable cloud-native systems using distributed architectures, APIs, event-driven patterns, data platforms, and resilient services.
I write about AI architecture, Generative AI, LLMs, RAG, Agentic AI, cloud architecture, distributed systems, and production engineering.
Applying Kafka, Pub/Sub, and Redis to scale LLM systems.
A practical decision framework for retrieval architectures used in RAG and agentic systems.
A comparative exploration of major language model ecosystems and their role in the future of AI architecture.
Providing technical feedback and perspective through professional review activities.
Sharing practical technology, cloud, architecture, and career knowledge with other professionals.
Documenting an ongoing journey through AI architecture, experimentation, and applied learning.