You will get I will build an AI agent to automate your business workflows
Rising Talent
Rising Talent
Project details
I build AI agents that automate your repetitive business workflows — not chatbots, real working systems that handle multi-step processes end to end.
My approach: I coordinate multiple LLMs (Claude, GPT-4) as an orchestra conductor. Each agent handles a specific part of your workflow — data intake, decision logic, action execution — with human-in-the-loop controls where you need them.
What makes this different: I don't just write code. I architect AI systems where models do the heavy lifting. I've built ZeroPrimeAI — a full AI platform with 27 design documents, custom QA hooks, and production infrastructure running on local GPU hardware (RTX 5090 + 5070 Ti).
Real proof: I completed a technical assessment for ModelVault (2 projects + 3 bonus challenges, delivered 3 days early). The agents I build come with monitoring, error handling, and documentation your team can maintain without me.
Tools: Claude API, OpenAI API, LangChain, Python, Docker, vector databases, custom orchestration. If your workflow involves documents, data processing, approvals, or any repetitive multi-step process — I can automate it with AI agents that work.
My approach: I coordinate multiple LLMs (Claude, GPT-4) as an orchestra conductor. Each agent handles a specific part of your workflow — data intake, decision logic, action execution — with human-in-the-loop controls where you need them.
What makes this different: I don't just write code. I architect AI systems where models do the heavy lifting. I've built ZeroPrimeAI — a full AI platform with 27 design documents, custom QA hooks, and production infrastructure running on local GPU hardware (RTX 5090 + 5070 Ti).
Real proof: I completed a technical assessment for ModelVault (2 projects + 3 bonus challenges, delivered 3 days early). The agents I build come with monitoring, error handling, and documentation your team can maintain without me.
Tools: Claude API, OpenAI API, LangChain, Python, Docker, vector databases, custom orchestration. If your workflow involves documents, data processing, approvals, or any repetitive multi-step process — I can automate it with AI agents that work.
AI Development Type
Deep Learning, Knowledge RepresentationAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$3,500
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 21 days | 28 days | 35 days |
Number of Revisions | 2 | 3 | 4 |
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 - 8:12 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.
Discovery & Architecture
I map your workflow end-to-end, identify which steps benefit from AI automation, and design the agent architecture — which LLMs to use, how agents communicate, and where human review fits in.
Build & Integrate
I build the AI agents using Claude/GPT APIs, connect them to your existing tools and data sources, implement error handling and fallback logic, and set up monitoring.