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Achievement: Pull Sharkx2

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ElBaldo1/README.md

Typing intro

Milano / Bruxelles

Website   LinkedIn   Email

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About

I am an AI Applied Engineer with a software engineering background in Python and TypeScript, building AI applications end to end: from business problem and stakeholder requirements to API integration and production deployment. I am strongest where LLM applications, RAG and agentic workflows meet reliable backend engineering.

Now: Product Engineer at EEIP, focused on AI applications in production. Open to selected conversations for AI applied engineering roles and applied AI projects.

Recruiter snapshot

What I bring How it shows up
Applied AI LLM applications, RAG and agentic workflows shipped to production, not demos
Full-stack execution From FastAPI backends and PostgreSQL/pgvector to API integrations and deployment
Business awareness I translate stakeholder requirements into shipped, measurable AI features
Ownership I own delivery end to end: discovery, prototyping, release, feedback-driven iteration
International profile Based around Milano / Bruxelles, comfortable in cross-functional contexts

Applied AI focus

  • LLM applications & RAG: retrieval pipelines, vector search with Qdrant, grounded generation
  • Agentic workflows: LangChain, LangGraph, Langflow, tool-use agents, structured outputs
  • Backend & integration: async FastAPI, PostgreSQL, pgvector, third-party API integration
  • Deployment & cloud: Docker, CI/CD (GitHub Actions), AWS
  • Delivery ownership: stakeholder discovery, requirements, prototyping, production deployment, iteration

I am useful when

  • A team needs an AI application taken from prototype to production, owned end to end
  • A manual, high-volume workflow needs to become an AI-driven system (e.g. lead generation, data processing)
  • RAG or agentic workflows need to be grounded in real business data and shipped reliably
  • Backend and integration work is needed to connect AI workflows to existing systems and data

Core capabilities

  • Applied AI: LLM applications, RAG, agentic workflows, LangChain, LangGraph, Langflow, Qdrant
  • Backend & integration: FastAPI, async, API integration, PostgreSQL, pgvector
  • Deployment & cloud: Docker, CI/CD (GitHub Actions), AWS
  • Programming: Python, TypeScript, SQL
  • Delivery: stakeholder discovery, requirements to release, feedback-driven iteration
AI applied engineering story

I like building AI applications from the inside out: start with the real business workflow, ground the system in real data, design the simplest reliable path from prototype to production, then iterate on real feedback.

At EEIP, I replaced a manual lead generation and outreach workflow with an AI application processing 1,000+ leads per day, using Python, LangChain, LangGraph and Langflow to build agentic workflows, with async FastAPI backends and RAG pipelines on Qdrant to ground the system in internal knowledge.

Engineering foundations

My software engineering background in Python and TypeScript keeps my AI work grounded: I can reason about APIs, data models, async backends, and deployment while connecting agentic workflows and RAG pipelines to real business outcomes instead of standalone demos.

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