Who is Fernando F. Azevedo?

Fernando F. Azevedo is a Senior Solutions Architect at Banco Itaú with 16+ years of experience across AWS, event-driven architecture, DevSecOps, Data Mesh, AI and financial systems.

What technical topics does Fernando work with?

Fernando works with AWS, Kubernetes, Kafka, Data Mesh, Amazon Bedrock, RAG, DevSecOps, observability, financial systems and architecture communication using C4, ADRs and trade-off analysis.

Is Fernando available for professional conversations?

Fernando is currently building at Banco Itaú and is open to thoughtful conversations about architecture, cloud, AI, engineering leadership, community, podcasts and technical collaboration.

Learning path · Trilha

AWS & Cloud

Serverless-first AWS done with a FinOps mindset: the services, trade-offs and cost levers that matter in production.

The path· 2

1Expert 9 minOne Org or Many? The Postmortem Nobody Wants to WriteThe choice between a single AWS Organization and multiple organizations looks strategic on paper but becomes operational at the worst possible moment — during an incident. This article reconstructs the most common failure pattern I observe in financial-grade environments and proposes concrete remediations grounded in the AWS Well-Architected pillars.
2Expert 10 minOracle HA on AWS: FSx for ONTAP as a Lever for Gradual ModernizationThe most relevant signal in this cycle for architects still carrying critical Oracle workloads is not to abandon them — it is to orchestrate them with cloud-native primitives that deliver real HA without forcing a rewrite. FSx for NetApp ONTAP changes the storage equation for Oracle on AWS in concrete ways. This briefing analyzes what changes in practice, the real trade-offs, and how to position this layer within a gradual modernization strategy.

Deep-dive studies

teardownTeardown: Resilient Network Graphs and the Next-Generation AI NetworkAn in-depth architectural analysis of the resilient graph-based data center networks AWS is building to support AI workloads at scale — covering topology, congestion control, energy efficiency, and the trade-offs that define the next generation of cloud infrastructure.adrADR: AWS Transform & AI Agents vs Traditional Modernization FactoryThis ADR evaluates the decision to adopt AWS Transform (with AI agents for .NET, Mainframe, VMware, and custom code) versus a traditional human-engineering modernization factory, or a hybrid approach. The analysis covers regression risk, test coverage, code ownership, security, total cost, and change governance in an enterprise-scale modernization program.design-docDesign Doc: Continuous Evaluation Suite for Agents with Bedrock AgentCoreLLM agents in production silently degrade as models, tools, and prompts evolve — without a continuous evaluation discipline, regressions reach users before they are detected. This document proposes a complete offline and online evaluation architecture using Amazon Bedrock AgentCore, with versioned datasets, CI/CD quality gates, runtime signals, and systematic adversarial testing.design-docDesign Doc: LLM Observability — from GPU Utilization to Response QualityThis document proposes an end-to-end observability architecture for LLM inference platforms running on Amazon SageMaker AI and Amazon Bedrock, covering everything from hardware metrics (GPU utilization, memory) to semantic response quality, behavioral drift, and per-tenant cost. The design integrates CloudWatch, Amazon Managed Grafana, prompt-level tracing, and automated regression alarms, with clear separation of concerns across collection, storage, evaluation, and alerting layers.adrADR: Cognito Multi-Region for Resilient AuthenticationThis ADR examines when and how to adopt multi-region User Pool replication in Amazon Cognito to reduce authentication downtime on identity platforms with high-availability requirements. It covers regional failover, customer-managed KMS keys, user synchronization, session and token impact, custom domains, and customer experience, with explicit reasoning on operational and cost trade-offs.adrADR: OpenSearch Serverless vs Dedicated Vector Database for Agentic RAGThis ADR evaluates vector search infrastructure options for a multi-tenant agentic RAG platform on AWS, comparing OpenSearch Serverless, dedicated vector databases (Pinecone, pgvector), and a self-managed hybrid search layer. The decision weighs cost, p99 latency, permission-based filtering, incremental ingestion, and native Bedrock Knowledge Bases integration.

Open source to explore

queue-advisor-pricing-appCompare SQS, Kinesis, EventBridge and MSK costs before committing to an AWS queue.aws-event-driven-finops-platformEvent-driven AWS banking reference architecture with FinOps, security, and a live frontend.aws-agentic-ai-reference-architectureAWS reference architecture for production agentic AI — security, observability, and DevSecOps.DeskbuddyESP32 touchscreen smart desk dashboard — firmware, web UI, and browser installer in one repo.solution-architecture-mcp-toolkitBilingual MCP toolkit for ADRs, threat modeling, and governed Well-Architected reviews.mcp-aws-solution-architectMCP server that turns any AI assistant into a copilot for AWS Solution Architects.