You will get I will build a production RAG pipeline with hybrid search for your AI
Rising Talent
Project details
I build production-ready RAG pipelines that let your team query internal documents, databases, and knowledge bases using natural language. No hallucinations, no black boxes — just accurate, cited answers from your own data.
What you get:
• Document ingestion pipeline (PDF, Docx, HTML, databases) with intelligent chunking
• Hybrid search combining semantic embeddings + keyword retrieval for maximum accuracy
• Reranking layer to surface the most relevant passages
• LLM integration (OpenAI, Claude, open-source) with grounded, cited responses
• Evaluation framework to measure retrieval quality and catch regressions
• Self-hosted deployment on your infrastructure — your data stays yours
I coordinate AI models (Claude, Cursor, Codex) to architect and build systems, not just write code. My approach: define the architecture, decompose the problem, direct the models. This means faster delivery and production-grade quality from day one.
Recent proof: completed a full technical assessment (2 projects + 3 bonus challenges) 3 days ahead of deadline for a senior AI role. I bring that same execution speed to freelance projects.
What you get:
• Document ingestion pipeline (PDF, Docx, HTML, databases) with intelligent chunking
• Hybrid search combining semantic embeddings + keyword retrieval for maximum accuracy
• Reranking layer to surface the most relevant passages
• LLM integration (OpenAI, Claude, open-source) with grounded, cited responses
• Evaluation framework to measure retrieval quality and catch regressions
• Self-hosted deployment on your infrastructure — your data stays yours
I coordinate AI models (Claude, Cursor, Codex) to architect and build systems, not just write code. My approach: define the architecture, decompose the problem, direct the models. This means faster delivery and production-grade quality from day one.
Recent proof: completed a full technical assessment (2 projects + 3 bonus challenges) 3 days ahead of deadline for a senior AI role. I bring that same execution speed to freelance projects.
AI Development Type
Deep Learning, Knowledge RepresentationAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 5 |
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 - 6:57 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
Analyze your data, define chunking strategy, select embedding model and vector DB. Deliver architecture doc.
Build & Integrate Pipeline
Implement ingestion, chunking, embedding generation, vector storage, and retrieval chain. Test with your data.