Available for opportunities

Dominick PescettoAI Engineer > CTV Platform Architect

Building production-grade AI infrastructure and cross-platform streaming apps for Roku, FireTV, Android TV, Tizen, and webOS.

~/portfolio $
$hermes deploy --production
$kubectl get pods -n production
$vllm serve --tensor-parallel 4 ...
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Production Systems That Scale

From AI agent orchestration to cross-platform CTV apps - here are the systems I've built that power real-world workflows at scale.

Featured

Hermes Agent Framework

AI Agent Orchestration System

Production-grade AI agent framework with multi-model routing, MCP integration, and a 600K+ node knowledge graph. Orchestrates intent classification, entity extraction, and tool execution across microservices.

ProblemNeed a scalable architecture where multiple AI models collaborate, context is maintained across sessions, and tools are dynamically dispatched based on intent - all with sub-second latency.
SolutionBuilt a modular agent framework with an Intent Router (BM25 classification), Entity Extractor, MCP server integration, and a unified knowledge graph. Deployed as 4+ microservices with health monitoring cron.
Outcome600K+ node memory index, 94% tool reduction via intent routing, sub-100ms classification, production deployment on Proxmox cluster.
PythonFastAPIMCPGraphRAGChromaDBBM25+3
View Sourcehermes-agent
Featured

CTV Platform Development

Cross-Platform Streaming Applications

Production streaming apps across all major smart TV platforms: Roku, FireTV, Android TV, Samsung Tizen, LG webOS, and Vizio. Includes SDK toolchains, CI/CD pipelines, and YouTube channel feed integration.

ProblemEach CTV platform has its own ecosystem, SDK, packaging format, and certification process. Maintaining feature parity across all platforms requires significant effort and specialized tooling.
SolutionBuilt a unified development infrastructure with GitHub Actions runners for each platform, shared component libraries, automated CI/CD pipelines via BrighterScript (Roku), Java/Kotlin (FireTV), and webOS CLI toolchains.
OutcomeProduction apps on 6 platforms, automated CI/CD for all, YouTube channel integration with 249+ videos indexed. GitHub Actions runner handling multi-platform builds.
RokuBrighterScriptFireTVAndroid TVTizenwebOS+4
View Sourcectv-platforms
Featured

CodeGraphContext (CGC)

Code Knowledge Graph Toolkit

MCP server and CLI toolkit that indexes codebases into knowledge graphs for intelligent code discovery, call graph analysis, data flow tracing, and automated bug detection.

ProblemUnderstanding large codebases requires traversing files, functions, and relationships manually. Existing tools don't provide semantic search across function definitions or cross-service dependency tracking.
SolutionBuilt a knowledge graph indexer that parses code into structured representations (nodes for functions, classes, imports; edges for calls, data flows). Added BM25 ranking, Cypher query support, and MCP integration.
OutcomeProduction toolkit used for cross-service analysis, hot-path detection (loop depth, memory allocations), architecture clustering. Supports full-text search with structural boosting.
PythonLSPCypherMCPChromaDBBM25+2
View Sourcecodegraph-context
Featured

Intel Arc B70 AI Toolboxes

GPU-Accelerated LLM Inference Infrastructure

Production Docker toolboxes for vLLM and llama.cpp on Intel Arc Pro B70 GPUs. Supports NVFP4/INT4 quantization, XPU optimization, and CI/CD pipeline for automated image publishing.

ProblemIntel ARC GPUs require special setup for LLM inference (XPU drivers, SYCL/vulkan). Deploying vLLM with tensor parallelism across 4 GPUs needs careful shared memory, security, and driver configuration.
SolutionCreated isolated Docker toolboxes for llama.cpp (SYCL + Vulkan) and vLLM with proper XPU environment variables. Configured --tensor-parallel-size 4, --shm-size 200g, --security-opt seccomp=unconfined for multi-GPU inference.
OutcomeDeepSeek V4-Flash deployment on Intel Arc Pro B70 with NVFP4 quantization (~68-85GB weights). ~25-45 tokens/sec generation, 5K-15K+ tokens/sec prefill. Automated CI/CD via GitHub Actions.
PythonvLLMllama.cppDockerIntel GPU (XPU)NVFP4+3
View Sourceintel-b70-toolboxes
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About

The Architect Behind the Systems

I'm Dominick Pescetto - a Senior AI Engineer building production-grade systems that actually work in the real world.

AI Infrastructure: local LLM deployment, agent frameworks, knowledge graphs. CTV Platforms: cross-platform streaming apps for every major smart TV ecosystem. Infrastructure: Proxmox clusters, Kubernetes, automated CI/CD.

Every project ships with comprehensive testing, production monitoring, and clean documentation. No drafts. No half-measures. Only verified, polished artifacts.

6+
CTV Platforms
600K+
Graph Nodes
4
Microservices
99.9%
Uptime
AI / ML
vLLMllama.cppLangChainMCP ServerGraphRAGRAGNER/BERTEmbeddingsQwen
Infrastructure
Proxmox VEDockerKubernetesAnsibleGitHub ActionsCloudflareOpenWRTSystemd
CTV Platforms
Roku (BrightScript)FireTV (Java/Kotlin)Android TV / Google TVSamsung TizenLG webOS CLIVizio SmartCast
Languages
PythonTypeScriptBashKotlinJavaBrightScriptSQL
Databases
ChromaDBSQLiteMySQLmem0aiRedisS3 / R2
DevOps
GitLinuxIntel Arc GPUGrafanaVS Code ServerPortainerNginx
Let's Build

Let's Build Something Together

Whether you need an AI agent orchestration system, a cross-platform CTV app, or production infrastructure - I deliver verified, polished results.

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