Paravision Lab is my independent engineering portfolio where I design and build GenAI systems focusing on LLM infrastructure, agent workflows, and reliable AI backends.
The portfolio highlights full-stack GenAI systems that not only implement these technologies but also demonstrate best practices in reliable architectures, asynchronous workflows, and modern AI infrastructure.
A set of end-to-end builds demonstrating agent orchestration, retrieval pipelines, streaming UX, and production-grade AI backends.
AI-powered search with intelligent query routing, Tavily web search, and Gemini LLM summarization. Features streaming responses and persistent sessions.
Key implementation
Personalized AI video ads: LangChain scripts, HeyGen avatars, async processing via Inngest. Tracks jobs and manages rendering workflows.
Key implementation
Converts topics to narrated short videos: scripts, TTS audio, AI images, captions. Async rendering with Remotion.
Key implementation
A structured approach used across product builds, emphasizing deterministic orchestration, system evaluation, and observable AI behavior.
Define the AI workflow, system constraints, user interaction patterns, and evaluation criteria before implementation.
Design orchestration flows, tool interactions, retrieval pipelines, and state management to ensure deterministic and debuggable AI workflows.
Implement AI pipelines with structured outputs, evaluation hooks, retries, and cost/latency controls.
Deploy systems with monitoring, logging, streaming support, and observability hooks for debugging and evaluation.
Designing GenAI systems that address real-world challenges in LLM orchestration, retrieval pipelines, state management, and scalable AI workflows.
Designing modular GenAI systems with clear separation between UI, API, and AI orchestration layers.
Building deterministic agent workflows with explicit control flow and tool routing.
Designing retrieval pipelines using vector search and grounded document retrieval.
Implementing real-time LLM responses using streaming inference and incremental output.
Designing AI workflows that maintain session memory, checkpoints, and conversation state.
Building AI agents that interact with APIs, databases, and external tools.
Designing pipelines that produce validated JSON or schema-based outputs for downstream automation.
Designing multi-step prompt pipelines for reliable reasoning and task execution.
Infrastructure and patterns demonstrated across portfolio builds
Docker · Redis · Inngest · GitHub Actions · LangSmith · Arcjet
Docker and Docker Compose for consistent local and deployed environments
FastAPI-based architecture supporting streaming inference and modular backend components
Inngest for background jobs, retries, and durable execution
Redis and Arcjet for rate limiting and request throttling
Environment secrets and runtime settings for reproducible deployments
Token-based LLM responses with incremental output delivery
Background workers and queue-based systems for long-running AI tasks
GitHub Actions pipelines for automated testing, builds, and deployments
Comprehensive tracing, structured logging, and evaluation via LangSmith
Schema enforcement, input validation, and output verification systems
Independent GenAI Systems Engineer
I design and build production-ready GenAI systems end-to-end — from LLM orchestration architecture to deployed full-stack applications.
My work spans agentic workflows, retrieval systems, streaming pipelines, and scalable AI infrastructure, with strong defaults around state management, evaluation, observability, and cost/latency control.
With 15+ years in applied AI/ML systems — across optimization, simulation, deep learning, and modern LLM architectures — I focus on building AI systems that are robust, scalable, and production-ready.
