Senior Backend Engineer (Java/Elixir/Python) turned AI Practitioner
I bridge the gap between Distributed Systems and Agentic AI. My core expertise lies in building resilient, event-driven backends (Kafka, Kubernetes) and applying those same architectural principles to build stateful, non-deterministic AI agents.
🏎️ ChatFormula 1 (Agentic AI)
- What it is: A multi-agent system that predicts F1 race outcomes and explains its reasoning.
- Why it matters: Moves beyond simple RAG by using LangGraph for cyclic reasoning. It implements ReAct patterns, self-reflection, and checkpointers for time-travel debugging.
- Tech: Python, Streamlit, LangChain, LangGraph, OpenAI, Vector Stores.
📡 synthetic-network-testing-microservice (Distributed Systems)
- What it is: A high-throughput network observability system.
- Why it matters: Demonstrates "Senior" backend architecture. It handles synthetic HTTP/ICMP probing at scale, decoupling ingestion from processing using Kafka for resilience.
- Tech: Java (Spring Boot), Kafka, PostgreSQL, Docker/K8s.
🗺️ geo-standardization-case-study (Data Engineering)
- What it is: A case study on normalizing and standardizing complex geospatial data.
- Why it matters: Clean data is the fuel for AI. This project showcases the data engineering rigor required before feeding data into LLMs or RAG pipelines.
- Tech: Python, Pandas, Data Normalization Algorithms.
| Domain | Technologies |
|---|---|
| AI & Agents | LangChain, LangGraph, RAG, Vector DBs, Prompt Engineering |
| Backend Core | Java (Spring Boot), Elixir (Phoenix), Python |
| Distributed Systems | Apache Kafka, gRPC, PostgreSQL, Redis |
| DevOps & Cloud | Kubernetes, Docker, AWS, Terraform |
- Exploring Multi-Agent Orchestration patterns.
- Deep diving into Graph RAG techniques.