Engineering / 02
Production machine learning is more than a model. It is the data, deployment, inference, evaluation, and operational discipline around it. I build the infrastructure that brings those pieces together.
System map / Architecture
A client support question crosses identity and retrieval boundaries before producing a cited draft. Evaluation and human approval remain distinct; an unsafe or unsupported answer returns to manual work.
Discuss this kind of systemInside the system
Select a node in the diagram to read its responsibilities, interfaces and failure behavior.
01 / Component
An authenticated support agent submits a customer question within an explicitly selected tenant and case.
02 / Component
The authorization service resolves permitted document sets and rechecks access when evidence is read.
03 / Component
Hybrid retrieval searches only authorized, versioned chunks and keeps source identifiers with every passage.
04 / Component
A bounded model request treats retrieved text as untrusted data and emits a draft with explicit source references.
05 / Component
A versioned evaluation policy checks citation support, sensitive-data handling and required escalation categories.
06 / Component
The support agent compares cited evidence with the case, edits the response and explicitly approves any customer communication.
07 / Component
Existing search and escalation remain available when assistance is unavailable, unauthorized or unhelpful.
08 / Component
Deidentified reviewer feedback and a held-out task set test candidate retrieval and prompt versions before promotion.
Recognize the situation?
What we can work on
Connect model tracking, orchestration, CI/CD, and Kubernetes deployment with MLflow, Airflow, and Kubeflow.
Build measured inference workflows around throughput, latency, hardware utilization, and reproducible evaluation.
Develop dependable ingestion and processing workflows across Postgres, Snowflake, streaming systems, and cloud-native services.
Design IAM boundaries, observable pipelines, and deployment controls for sensitive data and healthcare workflows.
Engineering notes
Real client engagements and the engineering behind them.
A real AI service traces CUDA warps, memory and Tensor Cores to avoid premature GPU expansion. Follow the architecture and a labeled capacity-cost model.
A real AI platform compares B200/B300 servers, hypervisors, storage and warm capacity, showing why GPU hosting value depends on more than device count.
Compare GPU rental pricing, neoclouds and colocation through a real business case. Include utilization, failure coverage and ownership in the cost model.
A real B200 deployment links KV cache, batching and vLLM vs TensorRT-LLM to cost per useful response, with explicit latency and quality assumptions.
A real forecasting deadline made CUDA matrix multiplication a business decision. Learn shared-memory tiling, correctness and application-level limits.
A real ranking service uses JAX softmax to study kernel fusion, numerical stability and GPU memory traffic, with a bounded end-to-end latency model.
A real forecasting company compares JAX, CUDA, GPUs and TPUs at the right layers, using compilation, transfer and run-time costs—not brand rankings.
A real forecasting business connects MLOps pipelines, model evaluation and rollback to release handoff effort without treating a notebook as production.
A real service desk reduced exception-handling work through durable agent states, safe retries and explicit tool approvals—not an autonomy promise.
A practical starting point
Understand the constraint. Build the smallest durable solution. Measure what changed. Leave the team with a system they can own.
Share what is running today, where it is getting in the way, and what needs to change.
Discuss ai infrastructure Explore engagement optionsA useful next conversation
A system, a delivery bottleneck, or an engineering opportunity. Tell me what you are building and where you want to go.
Let’s talkTechnical glossary: definitions, connected ideas and further reading.
Optional analytics off. Contact works either way.
