Paulo Henrique
Independent AI Consultant and former Engagement Manager
New to Contra
Paulo is ready for their next project!
Developed and optimized a large-scale search and retrieval system, improving ranking, relevance, and personalization using machine learning and NLP techniques.
Focused on transitioning from keyword-based retrieval to semantic and context-aware search, enabling better understanding of user intent.
Tech Stack
ML/NLP: TensorFlow, embeddings, ranking models
Backend: Java, Python
Infra: distributed systems (Google-scale / cloud clusters)
Search: Elasticsearch / custom ranking pipelines
Data: large-scale distributed storage systems
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Designed a multi-agent AI system capable of executing complex workflows across tools (APIs, databases, services), enabling automation of tasks such as reporting, research, and operational processes.
The system uses structured planning, tool-calling, and feedback loops to improve reliability — addressing common failures in naive agent implementations.
Tech Stack
LLMs: GPT-based models, function/tool calling
Frameworks: LangChain, LangGraph (or custom orchestration)
Backend: Python
Infra: Docker, Kubernetes
Data: vector DB + structured storage
Workflow orchestration: event-driven pipelines
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Built a real-time fraud detection system for transaction-heavy environments, combining rule-based logic with machine learning models to evaluate risk at the point of transaction.
The system processes high-throughput payment streams and assigns risk scores within strict latency constraints (<100ms), enabling immediate decisioning (approve, flag, block).
Tech Stack
ML: PyTorch, TensorFlow, anomaly detection models
Streaming: Kafka, AWS Kinesis
Backend: Python, Node.js
Infra: AWS, Kubernetes
Data: Redis (low-latency), PostgreSQL
Monitoring: real-time metrics + alerting systems
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Designed and deployed an enterprise-grade LLM copilot that integrates with internal data systems (CRM, analytics, ops) to provide real-time decision support for business teams.
The system goes beyond chat — it executes multi-step reasoning workflows (data retrieval → analysis → recommendation), enabling non-technical users to interact with complex data systems through natural language.
Tech Stack
LLMs: GPT-based APIs, prompt orchestration
Backend: Python, FastAPI, Node.js
Infra: AWS (Lambda, S3, ECS), Docker
Data: PostgreSQL, vector DB (Pinecone / FAISS)
Orchestration: LangChain / custom pipelines
Observability: Prometheus, logging pipelines
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