You will get set up your AI infrastructure with Docker, GPU, and local model deployment
Rising Talent
Project details
I build AI infrastructure the way I build everything else — by directing AI models to do the heavy lifting while I make the architecture decisions that matter.
My setup: RTX 5090 + RTX 5070 Ti running locally. I know GPU workloads because I run them daily on my own hardware. Docker, CUDA, model serving with Ollama and vLLM — this is my actual working environment, not something I read about.
What you get: containerized AI services that your team can deploy, scale, and maintain. Docker Compose or Kubernetes, GPU passthrough configured correctly, model endpoints that respond fast, and monitoring that tells you when something breaks before your users do.
I passed a technical assessment building two full AI projects plus three bonus features in under a week. Infrastructure was part of that — Docker containers, CI/CD, the full stack.
Whether you need a single model serving endpoint, a multi-GPU training setup, or a complete MLOps platform with monitoring and auto-scaling — I'll design it, build it, and document it so your team owns it completely.
My setup: RTX 5090 + RTX 5070 Ti running locally. I know GPU workloads because I run them daily on my own hardware. Docker, CUDA, model serving with Ollama and vLLM — this is my actual working environment, not something I read about.
What you get: containerized AI services that your team can deploy, scale, and maintain. Docker Compose or Kubernetes, GPU passthrough configured correctly, model endpoints that respond fast, and monitoring that tells you when something breaks before your users do.
I passed a technical assessment building two full AI projects plus three bonus features in under a week. Infrastructure was part of that — Docker containers, CI/CD, the full stack.
Whether you need a single model serving endpoint, a multi-GPU training setup, or a complete MLOps platform with monitoring and auto-scaling — I'll design it, build it, and document it so your team owns it completely.
AI Development Type
Deep Learning, Software MaintenanceAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$4,000
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 10 days | 21 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Javier
AI Systems Engineer | RAG, AI Agents & LLM Integration | 20yr Infra
Valladolid, Spain - 7:58 pm local time
What I deliver:
— RAG pipelines: ingestion, chunking, embeddings, hybrid search (vector + BM25), retrieval quality evaluation
— Multi-agent systems: distributed governance, lineage tracking, tool invocation, safety guardrails
— LLM orchestration: multi-model evaluation, prompt engineering, production observability (tracing, tokens, latency, anomaly detection)
— Python backends: FastAPI, async services, PostgreSQL/pgvector, Redis, Docker deployments
Evidence — not claims:
In 2025 I completed a technical assessment for an AI startup (ModelVault): 2 projects + 3 bonus challenges, delivered 3 days before the deadline. The system included Mistral 7B inference, real-time GPU dashboard, HTTP telemetry, benchmarks, and concurrency control. The hiring manager said: "I believe you would be a great fit for our team." Code on GitHub.
I've been building my own local multi-agent AI ecosystem for over a year. Details are confidential, but the numbers speak: 22,000+ lines of code generated by directing AI, 240+ real experiments, 6 coordinated PostgreSQL databases, production-grade LLM observability, and multi-judge evaluation across 4 different models.
How I work:
I research and decompose before building. I design the full solution — components, connections, failure points — and document it. Then I build with AI-native tools: multiple models generate and review each other's work. If something works but it's a shortcut, I redo it. 20 years in production infrastructure means I know what breaks at 3am and I build to prevent it.
Communication:
I work through written channels — Slack, email, detailed project documentation. My technical writing in English is proven: 100+ design documents and full ModelVault documentation in English. Frequent updates, clear READMEs, detailed project plans.
My stack:
Python (FastAPI, asyncio) · PostgreSQL/pgvector · Redis · Docker · Linux · CUDA · Bash · LLM APIs (Claude, GPT-4, Mistral, Llama, Qwen) · RAG (embeddings, vector search, hybrid retrieval) · Prompt engineering · GPU infrastructure (RTX 5090 + RTX 5070 Ti)
Availability:
Available to start within 48h. Part-time (20 hrs/week), flexible schedule — can extend for time-sensitive projects. Based in Spain (CET), US-compatible hours.
Steps for completing your project
After purchasing the project, send requirements so Javier can start the project.
Delivery time starts when Javier receives requirements from you.
Javier works on your project following the steps below.
Revisions may occur after the delivery date.
Infrastructure Audit & Design
Review your existing setup, identify bottlenecks, and design a Docker-based architecture optimized for your AI workloads and hardware.
Build & Deploy Containers
Set up Docker containers, configure GPU access, deploy model serving endpoints, and wire up monitoring and logging.