JEEConf, Kiev, Ukraine

Wednesday, May 15, 2019 | 2 minute read

Ricardo Ferreira

This year I had the honor of speaking at JEEConf; one of the most important Java conferences in Europe hosted in the heart of Ukraine. The conference held two days full of content related to Java and related technologies.

I was really impressed with the number of smart people that I could shake hands and meet at the conference. One of the sessions that I presented there was Tips & Tricks about Apache Kafka in the Cloud for Java Developers, that aimed to show some of the details that developers building Java applications for Apache Kafka should know before venturing themselves in moving their workloads to Cloud.

It was one of the first sessions of the morning, and I was glad to see the room packed with peopled interested in Apache Kafka. Even happier when I found out that 90% of the people there was familiar and/or already working with it.

The slides from this presentation is here:

https://speakerdeck.com/riferrei/tips-and-tricks-about-apache-kafka-in-the-cloud-for-java-developers-e092c220-9869-413d-a55a-a726b6800afd

Of more importantly… the recording of the presentation is also available:

https://www.youtube.com/watch?v=I0Cst1H6edQ

Another talk that I delivered at this conference was Implementing Distributed Tracing ‘Like a Boss’ in your Kafka Deployments ; where I showed how to implemented tracing using the OpenTracing extensions for Kafka, as well as the extensions that I created for KSQL, Connect and REST Proxy.

The slides from this presentation is here:

https://speakerdeck.com/riferrei/implementing-distributed-tracing-like-a-boss-in-your-apache-kafka-deployments

…as well as the recording of the presentation:

https://www.youtube.com/watch?v=9WqdqmQcaro

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Open Source

I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.

I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.

Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work since 2018. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.

Recordings of my talks live on my YouTube channel, and the code I write for talks, demos, and tutorials is on my GitHub.

Who am I?

I work at the intersection of distributed systems, AI, and data infrastructure, turning complex technology into things developers can understand, adopt, and build with.

Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer; contributing to LangChain4j and RedisVL for Golang; and figuring out how to make AI agents secure enough to ship.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years, moving data fast, at scale, close to compute, watching where systems break; now applied to vectors and agents. I have worked on event streaming with Apache Kafka and Flink at AWS and Confluent; observability at Elastic; as well as RDBMS, NoSQL, and Big Data at Oracle. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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