You will get integrate LLMs (Claude, GPT) into your existing app or workflow
Rising Talent
Project details
I don't write code line by line — I direct AI models to build production systems. My approach to LLM integration is different: I design the architecture, craft the prompts, and orchestrate Claude, GPT, or open-source models to deliver working solutions fast.
What you get: a clean LLM integration into your existing app or workflow. API connections, optimized prompts, structured outputs, error handling, and documentation your team can actually maintain.
I passed a technical assessment building two full AI projects plus three bonus features in under a week. That's the pace and quality I bring to every engagement.
Tools: Claude API, OpenAI API, Python, Docker, LangChain, local models (Ollama, vLLM). I work with whatever fits your stack.
Whether you need a single API endpoint that summarizes documents, a multi-model pipeline that classifies and routes data, or a full RAG system with vector search — I'll architect it, build it, and make sure it works in production.
What you get: a clean LLM integration into your existing app or workflow. API connections, optimized prompts, structured outputs, error handling, and documentation your team can actually maintain.
I passed a technical assessment building two full AI projects plus three bonus features in under a week. That's the pace and quality I bring to every engagement.
Tools: Claude API, OpenAI API, Python, Docker, LangChain, local models (Ollama, vLLM). I work with whatever fits your stack.
Whether you need a single API endpoint that summarizes documents, a multi-model pipeline that classifies and routes data, or a full RAG system with vector search — I'll architect it, build it, and make sure it works in production.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$1,500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 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 - 5:56 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
Review your codebase and requirements. Define which LLM fits best, design the integration architecture, and plan the prompt strategy.
Build & Test Integration
Connect the LLM API, implement prompt templates, add error handling and fallbacks. Test with real data and iterate on prompt quality.