smile - Hello, I’m an AI engineer focused on ...
Hello,
I’m an AI engineer focused on building production systems around modern AI platforms, mainly on backend and infrastructure.
Recently I’ve been working across:
* LLM platforms (OpenAI, open-weight models, fine-tuning, eval pipelines)
* Agent frameworks (multi-step planning, tool use, orchestration)
* RAG systems (vector search, hybrid retrieval, reranking, memory design)
* Real-time systems (WebSockets, streaming inference, event-driven architectures)
* Multimodal AI (text, image, audio pipelines)
* MLOps and deployment (model serving, monitoring, scaling, cost control)
* Data pipelines (embedding workflows, indexing, sync systems)
* AI + Web3 integrations (on-chain/off-chain coordination, subnet-style systems)
My focus is on making systems actually work in production.
Reliability, latency control, reducing hallucinations, and designing architectures that scale beyond demos.
Currently interested in how people here are approaching:
* evaluation and reliability for agent workflows
* long-context handling and memory systems
* hybrid architectures combining LLMs with deterministic logic
* tradeoffs between latency, cost, and output quality
Open to exchanging ideas and approaches with others working on similar problems.
I’m an AI engineer focused on building production systems around modern AI platforms, mainly on backend and infrastructure.
Recently I’ve been working across:
* LLM platforms (OpenAI, open-weight models, fine-tuning, eval pipelines)
* Agent frameworks (multi-step planning, tool use, orchestration)
* RAG systems (vector search, hybrid retrieval, reranking, memory design)
* Real-time systems (WebSockets, streaming inference, event-driven architectures)
* Multimodal AI (text, image, audio pipelines)
* MLOps and deployment (model serving, monitoring, scaling, cost control)
* Data pipelines (embedding workflows, indexing, sync systems)
* AI + Web3 integrations (on-chain/off-chain coordination, subnet-style systems)
My focus is on making systems actually work in production.
Reliability, latency control, reducing hallucinations, and designing architectures that scale beyond demos.
Currently interested in how people here are approaching:
* evaluation and reliability for agent workflows
* long-context handling and memory systems
* hybrid architectures combining LLMs with deterministic logic
* tradeoffs between latency, cost, and output quality
Open to exchanging ideas and approaches with others working on similar problems.
