Senior AI/ML engineer with 7+ years across data science, ML engineering, and applied AI, plus PhD research experience. Builds and ships production GenAI and ML systems across React/Next.js frontends, FastAPI and TypeScript backends, retrieval/reranking, agent runtimes, evaluation, and operational reliability.
Skills
GenAI and LLM SystemsOpenAI APIs · Azure OpenAI · LlamaIndex · Agentic RAG · ReAct Tool-Using Agents · DSPy · PydanticAI · MCP Integration · Tool Calling and Orchestration · Structured Outputs · Metadata Filtering · Hybrid Retrieval and Reranking · Memory and Context Management · Vercel AI SDK
Led redesign of bid-optimization and forecasting ML systems for digital marketing workflows.
Built Python and SQL pipelines for production forecasting, anomaly detection, and decision support on Snowflake and Aurora-backed marketing data.
Partnered with business stakeholders to align model behavior with campaign KPIs and operational constraints.
ML / Computer Vision Consultant
ConstructIN, Remote, Brazil
Led production computer-vision delivery for construction monitoring and analytics.
Coordinated model development, validation, and delivery workflows with client teams.
Lead Data Scientist
Dataside, Remote, Brazil
Promoted to Lead Data Scientist; mentored team members and owned client-facing discovery, solution design, and technical delivery across multiple accounts.
Standardized evaluation, documentation, deployment, and stakeholder-alignment routines so ambiguous business problems became measurable ML roadmaps, data requirements, and KPIs.
Designed and shipped agentic RAG systems with tool-augmented retrieval, stateful memory, and graceful degradation for autonomous enterprise knowledge assistants (Azure OpenAI, LlamaIndex ReAct workflows, PostgreSQL/pgvector).
Built research-assistant and ranking workflows with dynamic question generation, document-scoped search, hybrid retrieval modes, rank fusion, and source-aware answers for autonomous answer quality.
Hardened the retrieval layer with safe vector-store initialization, advisory-lock-based schema setup, embedding-dimension safeguards, and non-destructive defaults for live indexed data.
Designed autonomous fallback paths and regression QA workflows — CLI probes and Playwright-backed browser validation — so assistants remained usable when orchestration dependencies or credentials were unavailable.
Delivered full-stack agentic applications with FastAPI backend and React frontend, deployed to Azure Container Apps with Grafana observability and Docker Compose orchestration.
Delivered unsupervised and decision-science solutions including clustering/segmentation, anomaly detection with SHAP-based explanations, experimentation/A-B testing, and hybrid NLP pipelines combining TF-IDF, BM25, embeddings, UMAP, HDBSCAN, and vector search.
Architected production ML pipelines with PySpark, SQL, Azure, Databricks Jobs, Azure DevOps CI/CD, MLflow model registry, containerized train/predict workflows, and scheduled retraining or model-renewal loops.
Lead Engineer
HopHR, Remote, Brazil
Led Moon's production candidate-search platform, unifying retrieval, scoring, reranking, and explainable output so ambiguous hiring requests could become auditable shortlist decisions.
Implemented backend APIs, async workflows, and runtime infrastructure on Supabase/PostgreSQL with Redis-backed caching and queues to support reliable, high-throughput hiring operations.
Improved release safety through CI/CD hardening, test-driven refactors, and stronger observability across search, streaming, and agent-runtime components.
Designed production AI search architecture combining Elasticsearch filters, semantic retrieval, multi-criteria reranking, and LLM-based assessments to improve shortlist quality while keeping results inspectable by recruiters.
Built chat-driven hiring workflows with planning, tool orchestration, memory-aware context injection, and AI SDK-compatible SSE streaming across backend and product surfaces.
Designed and operated a PydanticAI-centric multi-agent runtime: specialist profiles, delegated subtasks, memory-aware shortlist re-evaluation, and feedback-triggered workflow adaptation.
Built persistent LLM memory and session-aware context-injection infrastructure for candidate-search learnings with confidence-scoped retrieval across sessions.
Strengthened runtime observability with request correlation, trace propagation, safe logging/redaction, and metrics/tracing patterns suited for debugging long-running agent workflows.
Developed model-to-filter translation that turns learned SHAP/feature-importance signals into executable Elasticsearch filter clauses.
Partnered with recruiters and hiring managers to convert ambiguous role requirements into testable search/ranking hypotheses.
Data Scientist
Poatek IT Consulting, Porto Alegre, Brazil
Delivered DS/ML projects across optimization, NLP, computer vision, and risk modeling.
Senior AI Researcher (ML for Health)
Samsung Research Brazil, Campinas, Brazil
Led wearable-health ML feature development deployed globally in Galaxy Watch products.
Delivered memory-efficient inference designs for constrained devices and real-time operation.
Coordinated cross-functional research-to-product delivery with HQ stakeholders.
AI Engineer
SingularityAI (Part-Time), Remote, Brazil
Led end-to-end development of AI voice assistant with real-time transcription, TTS generation, and persona-aware RAG responses.
Built YouTube$\rightarrow$ChromaDB pipeline with AssemblyAI transcription, speaker diarization, and metadata-rich chunking for knowledge-base extraction.
Implemented LangGraph agents with PostgreSQL checkpointing for persistent conversation state across sessions.
Integrated ElevenLabs TTS, AssemblyAI STT, and Ultravox for real-time voice conversation with audio event handling (interruptions, pauses).
Designed PCA-based scope analysis and concurrent 22-attribute persona extraction for personalized, personality-adjusted responses.
Built MinIO-synced ChromaDB vector store with Redis caching for production-ready retrieval infrastructure.
LLM Consultant
Vortigo (Part-Time), Remote, Brazil
Built internal assistant chatbots grounded on proprietary code and spreadsheet knowledge sources.
Delivered retrieval and prompt pipelines with production guardrails for repeatable workflows.
Education
PhD in Computer Science (PhD, Machine Learning / Optimization)