Marcello B.

🥇 Top Rated on Upwork | 5+ Years Delivering Production Systems AI Systems Architecture • Software Engineering • AI Orchestration • Agentic Systems • AI Ops Software Engineering is not dead. It's more important than ever. AI can generate code. Engineering makes it work. In 2026, and increasingly in 2027, writing code is no longer the primary bottleneck for many software projects. The real challenge is designing, integrating, orchestrating, validating, and maintaining systems that actually work in production. AI can accelerate implementation. But generating code is not the same as engineering a reliable system. Architecture matters. Modularity matters. Validation matters. That's where I focus my work. What I Do I'm a Software Engineer and AI Systems Architect specializing in production-grade AI infrastructures, autonomous workflows, and modular software systems. I design and implement environments where LLMs, AI agents, databases, APIs, automation pipelines, and backend services operate as coordinated, independently testable components. My work goes beyond connecting AI models or assembling frameworks. I establish the architecture, define component responsibilities, implement integrations, validate execution, and ensure that systems remain observable, maintainable, and under control. Core Capabilities Software Architecture & Modularization: system decomposition, component boundaries, API contracts, dependency management, and independently testable modules. AI Orchestration & Agentic Systems: multi-agent architectures, tool execution, controlled workflows, bounded autonomy, and failure recovery. RAG & Knowledge Engineering: document ingestion, parsing, chunking, embeddings, vector databases, retrieval, evaluation, and grounded responses. LLM Infrastructure: external model APIs, local LLM deployments, inference integration, GPU-aware environments, routing, and fallback strategies. AI Ops & DevOps: Linux infrastructure, monitoring, logging, deployment automation, backups, rollback, and operational reliability. Data Engineering & Automation: scraping, ETL pipelines, data normalization, indexing, API integration, webhooks, and event-driven workflows. Deterministic Engineering: Bash and Python automation, explicit execution contracts, reproducible processes, and predictable system behavior. My Engineering Philosophy AI is a component of the system, not the architecture itself. Not every problem requires an LLM. Not every workflow requires an agent. Not every integration requires another framework. I prioritize deterministic execution wherever possible and introduce AI where reasoning, semantic interpretation, or language generation adds measurable value. My engineering approach is based on clear principles: Decompose before building. Define responsibilities and interfaces before integration. Validate individual components before scaling the system. Measure execution instead of trusting demonstrations. Design for failure, recovery, and maintainability. Finish what was defined instead of expanding scope indefinitely. I favor modular, composable, vendor-independent architectures over unnecessary abstraction and technological complexity. The goal is not to build the most impressive stack. The goal is to engineer the simplest reliable system that satisfies the actual requirements. Technology Stack Languages & Backend: Python, Bash, FastAPI, PHP, JavaScript, Node.js AI & LLMs: OpenAI, Cohere, external LLM APIs, local and self-hosted models, RAG, agentic workflows Data & Retrieval: PostgreSQL, Redis, Solr, vector databases, embeddings, semantic search Infrastructure & Operations: Debian Linux, Docker, Nginx, Git, CI/CD, systemd, automation scripts Extended Experience: Perl, HTML/CSS, Linux systems administration, legacy system integration Delivery & Accountability I prefer milestone-based engineering with clearly defined deliverables, acceptance criteria, and verifiable results. Complex projects begin with architecture and scope definition, followed by modular implementation, testing, integration, documentation, and operational handover. A successful delivery is not simply working code. It is a system that can be tested, understood, maintained, and operated without depending indefinitely on its original developer. Let's Talk Engineering If you're building an AI platform, integrating agents into existing infrastructure, modernizing automation, or turning an AI prototype into a production system, I can help establish the architecture and execute the engineering. I don't sell prompts or technological hype. I deliver engineered systems with measurable outcomes. Mike Niner Bravog AI Systems Architecture • Software Engineering • AI Orchestration • Agentic Systems • AI Ops Languages: English (Fluent) | Portuguese (Native) | Spanish