AI Engineer Building Production-Grade Agentic Systems
VITOR
SILVA
I design, build, and deploy scalable, observable, and cost-efficient AI agents that deliver real-world business value.
From stateful multi-agent orchestrations with LangGraph to high-fidelity RAG pipelines, I turn complex requirements into reliable software.
AI systems with clear business value.
Agents, retrieval pipelines and operational tools balancing technical depth with real user outcomes.
38 repositories · 245 commits
Production AI · Active development
Engineering Product,
Not Just Code.
With 15+ years of mission-critical operations experience, I don't just write scripts—I architect resilient systems. I've spent over a decade managing high-pressure logistics where downtime means total failure. That operational maturity is the foundation of my engineering.
I focus on systems that are observable, gracefully degrading, and maintainable. I build solutions to solve the real business problem, not just the technical challenge.
I operated for 15 years in zero-tolerance-for-failure environments. That mindset translates to defensive coding, robust error handling, and architectures built for resilience.
I understand business processes because I managed them for over a decade. I design solutions that deliver real-world outcomes.
Architectures that
generate business value.
The real challenge isn't building AI — it's trusting it in production. My projects target governance, guardrails and hallucination control.
MestreGrana
High risk of hallucination and lack of governance in generative AI for financial advice.
Architecture:┌─────────┐ ┌──────────────┐ ┌────────────────┐
│ User │───▶│ FastAPI │───▶│ LangGraph │
└─────────┘ │ Gateway │ │ Orchestrator │
└──────────────┘ └───────┬────────┘
┌───────────────────┼───────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ RAG │ │ Judge │ │ Governance │
│ Embeddings │ │ Multi-LLM │ │ & Auditing │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
└───────────────────┼───────────────────┘
▼
┌──────────────────┐
│ Validated Answer │
└──────────────────┘
Technical Solution:
- Stateful orchestration via LangGraph for long-term memory.
- "Judge" LLM system to audit answers (Guardrails).
FluencyForge
Teaching personalization at scale hits LLM context limits.
Architecture:User Request ↓ FastAPI Gateway ↓ LangGraph Orchestrator ├── Context Retrieval (Vector DB) ├── Curriculum Agent └── Assessment AgentSolution:
- Dynamic RAG coupled with stateful memory to maintain historical context.
TwinRank AI
Popularity-based recommendation systems fail in personalization.
Tech Stack:PyTorch, FastAPI, DVC, MLflow
Aether Oncology
Robust clinical data integration for multi-platform interfaces.
Tech Stack:FastAPI, Python, React, Flutter, Node.js
VektorWork
Freelancers depend on high-cost cloud tools for complex workflows with no data ownership.
Tech Stack:n8n, Docker Compose, PostgreSQL, Redis
RetentIA
High churn rates in SaaS platforms due to reactive support.
Tech Stack:Python, Scikit-learn, XGBoost, FastAPI
Harmoniz.AI
Scalably correlating complex biometric data.
Tech Stack:Python, Pandas, LLM Pipeline
AIClinicOS
Modern clinics need intelligent OS to manage data and patient care efficiently.
Tech Stack:Next.js, Tailwind, Supabase
Engineering Highlights.
Operational maturity.
Cutting-edge stack.
Most AI Engineers have the code. Few have 15 years of mission-critical ops teaching real systemic resilience.
Reliability mindset forged through high-pressure operations and problem-solving in environments that do not tolerate failures.
- SLA guarantee and large-scale operational continuity
- Technical focal point for corporate system implementation
- Analytical profile for real-time failure mitigation
- Formal foundations in algorithms, systems and distributed computing
- Complementary tracks: MLOps, FastAPI and Data Engineering
- MLOps, model deployment and ML pipelines in production
- Scalable AI system architecture and model governance
AI Engineer working end-to-end across architecture, backend and ML systems.
- Stateful agentic systems with memory and LangGraph orchestration
- FastAPI services with async patterns and high performance
- RAG pipelines designed for real use cases with semantic precision
Tools.
Not just buzzwords.
Global freelancer.
Available now.
I work remotely with clients in Brazil and worldwide. Production-grade AI stack, product-quality delivery.
Architecture and development of stateful agents with memory, multi-agent orchestration and robust decision flows.
High-fidelity semantic retrieval pipelines for Q&A, support and enterprise knowledge bases.
Multi-LLM audit systems that mitigate hallucinations before they reach the user. Cost control and groundedness.
High-performance async backends integrating AI models, databases and external services.
Architecture review, model selection, cost vs. accuracy trade-offs and implementation roadmap for teams adopting AI.
Remote, based in Brazil — available for clients in Brazil and worldwide. Fluent English for technical communication.
Reach out via email or LinkedIn. Describe the business problem and technical context. Within 24h we schedule an alignment call to define scope, timeline and budget.
Designs and implements AI systems for real use: autonomous agents, RAG pipelines, hallucination guardrails, ML APIs and LLM integrations (GPT, Gemini, Llama).
Yes. I serve clients remotely in any country. Technical communication in Portuguese and English. Available for international contracts via Deel, Remote or direct.
A regular chatbot answers from what the model was trained on — it can hallucinate or be outdated. RAG retrieves real information from your data before answering, ensuring accuracy and traceability.
Let's build the
next level?
Open to AI Engineer positions. AI Engineer working end-to-end across architecture, backend and ML systems. Professional maturity + cutting-edge AI stack for your team.