Develop and optimize applications based on Large Language Models (LLMs), ensuring response quality and prompt efficiency;
Agent Orchestration: Design and implement complex flows and multi-agent systems using LangChain and LangGraph for reasoning automation;
Data Architecture for AI: Implement Retrieval-Augmented Generation (RAG) strategies, including vector databases and indexing techniques for semantic search;
Backend Development: Build robust, high-performance APIs in Python (FastAPI/Django) to serve AI models at scale;
Cloud Scalability: Develop and deploy solutions using the AWS ecosystem, focusing on AI services, serverless compute and containers;
Quality and Monitoring: Establish testing standards for models (evaluation of hallucinations, latency and cost) and ensure the development lifecycle (LLMOps).

Advanced Python: Deep expertise in the language, including concurrency, static typing and software engineering best practices (SOLID, Clean Code);
AI Frameworks: Solid experience building chains with LangChain and cyclical/workflow graphs with LangGraph;
LLM Providers: Experience integrating models via API (OpenAI, Anthropic) and via AWS Bedrock;
Cloud Computing (AWS): Practical knowledge of Lambda, ECS/EKS, S3 and databases (RDS/DynamoDB);
Software Engineering: Proficiency with Git, Docker and CI/CD pipelines applied to backend projects;
LLMOps: Familiarity with model monitoring tools such as LangSmith, Weights & Biases or Arize Phoenix;
Fine-tuning: Basic knowledge of fine-tuning open-source models (Llama, Mistral) and PEFT/LoRA techniques;
Infrastructure as Code: Experience with Terraform or AWS CDK for provisioning AI environments;
AI Security: Familiarity with prompt injection mitigation practices and protection of sensitive data (LGPD
Brazilian General Data Protection Law).

Python Development, Gen AI

Key skills