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 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.
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.
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→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.
Senior Data Scientist
Condati, Remote, USA/Brazil
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.
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.
Data Scientist
Poatek IT Consulting, Porto Alegre, Brazil
Delivered DS/ML projects across optimization, NLP, computer vision, and risk modeling.