Profile material
Sanva Engineering Profile Notes
This page summarizes Sanva’s technical experience, including project background, ownership, technical tradeoffs, delivery outcomes, and lessons learned.
Positioning
A full-stack product engineer with deep mobile roots
Sanva has 13 years of software engineering experience, with an early foundation in iOS and lower-level client work: Objective-C, Swift, audio and video codecs, OpenGL ES with C++ rendering, high-performance SDKs, and payment-related modules.
That background shaped a practical engineering style: define layers and contracts early, then deal with performance, state, failure modes, and release validation before they become production problems.
Full stack
From client engineering to backend and cloud delivery
Later work expanded into Go / Gin services, PostgreSQL, Redis, gRPC, realtime messaging, WebSocket subscriptions, and cross-platform clients. The scope was not just one endpoint; it covered API contracts, client state, and delivery risk together.
Cloud delivery experience includes containerized deployment, cluster orchestration, ingress design, health checks, rollback planning, and maintenance windows, with emphasis on migration planning, observability, and release-risk control.
AI products
Turning LLM, RAG, and agent orchestration into shippable flows
In recent years, multiple AI apps were built end to end, spanning product definition, UI design, mobile implementation, backend services, model integration, subscriptions, and release preparation.
Key areas include multi-model integration, RAG, long-term memory, conversational product experience, prompt engineering, consent-aware data handling, and multi-agent coordination. The focus is on turning these capabilities into product systems that can be reviewed, accepted, and maintained over time.
Delivery
Release work needs evidence before and after launch
The delivery style favors repeatability: scaffolds, type checks, builds, page acceptance, store assets, review preparation, and post-release checks are turned into stable steps where possible.
Python is commonly used for data cleanup, automation scripts, batch checks, and toolchain glue. TypeScript, Go, and shell cover frontend, backend, and operations automation.
Portfolio
Work centers on long-term products and apps
The portfolio spans productivity, creative, health, education, and lifestyle themes, including career expression, meeting notes, speech practice, language practice, pixel assets, pet care, and gift recommendation flows.
These projects are best discussed through background, ownership, technical tradeoffs, outcomes, and reflection because they show the full path from user problems to product design, AI capabilities, mobile experience, subscriptions, and maintenance.
Collaboration
Where the experience fits best
For mobile complexity, iOS internals, backend contracts, AI productization, RAG, agent orchestration, app release, CI/CD, or cloud delivery, the experience usually supports both architectural and execution-level discussion.
If a project only asks for short-term feature stacking, the first step is to clarify boundaries, acceptance criteria, and maintenance cost before choosing native, cross-platform, managed services, or custom infrastructure.