Yuri Marca
Data Scientist & AI/ML Engineer
Florianópolis, Brazil
Download CV LinkedIn GitHub Scholar Email
yurimarca@gmail.com · linkedin.com/in/yurimarca · github.com/yurimarca
I build agentic AI systems and the production infrastructure around them — compressing the cycle from research prototype to deployed service. Recent work spans multi-agent workflows with Google ADK and LangGraph, retrieval-augmented generation on Vertex AI, and low-latency voice agents, all backed by automated LLM-as-Judge evaluation harnesses.
Before moving into applied AI engineering, I spent six years in academic research on Bayesian optimization, Monte Carlo Tree Search, and multi-objective evolutionary algorithms, producing internationally recognized results including a Best Student Paper Award at EMO 2019.
I hold a Master’s in Engineering from Shinshu University as a MEXT Government Scholar, with international academic experience across Japan, the United Kingdom, and Canada.
Experience
Zazmic Inc
AI/ML Engineer
- Agentic AI systems — Architected production multi-agent workflows with Google ADK and LangGraph, including ReAct agents for NL2SQL, Human-in-the-Loop state management, and autonomous generators ingesting multimodal unstructured data.
- Real-time voice AI — Engineered a low-latency bidirectional voice agent on WebSockets, FastAPI, and the Gemini Live API, with asynchronous tool definitions for hands-free interaction.
- Retrieval-augmented generation — Built production RAG systems on GCP Vertex AI, grounding enterprise knowledge retrieval in live document corpora with citation-traceable outputs.
- Evaluation & agentic CI/CD — Implemented LLM-as-Judge evaluation harnesses with dual tracing (LangSmith/Langfuse + OpenTelemetry); shipped containerized agentic services to Cloud Run via GitHub Actions.
Dry Ground AI
AI Engineer
- Developed and deployed AI voice agents handling real estate calls, integrating tools to retrieve live information, send messages and email, and automate client interactions.
- Built a data science workflow for user profile classification and lead identification, supporting strategic sales initiatives.
WEG
Data Scientist
- Wind turbine anomaly detection — Deployed a production system on AWS SageMaker anticipating failures up to two weeks ahead and flagging ~70% of maintenance interventions, with a companion explainability API for root-cause diagnostics.
- End-to-end MLOps — Built a reproducible pipeline (MLflow, Docker, Kubernetes, AWS) covering automated training, versioning, deployment, and live Grafana/InfluxDB monitoring with full experiment lineage.
- Deep generative modeling — Initiated PyTorch VAE research for turbine fault classification using latent-space health indicators and reconstruction-error thresholding over SCADA signals.
- LLM fine-tuning research — Led technical scoping to domain-adapt an open-weights LLM via SFT for NL-to-structured IoT workflow compilation; proposal approved by corporate leadership.
Macnica DHW
Data Scientist
- Smart sensors — Developed an SVM classification pipeline for vibration sensor data, applying NSGA-II to optimize the accuracy/compute trade-off for edge deployment.
- Computer vision APIs — Built a dual-layer video anonymization API (OpenCV + DBSCAN for motion-based person detection, ResNet-50-FPN as verification layer) tuned for low frame-rate surveillance footage.
- IoT development — Led end-to-end product development with full Docker containerization of the edge-to-cloud stack (MQTT, PostgreSQL, InfluxDB, Grafana), including on-device FFT computation.
SSE Gridtech
R&D Engineer · Internship
- Developed a long-range LoRaWAN communication system to optimize smart metering infrastructure and reduce GSM dependency.
Selected Work
Technical write-ups and code from projects I’ve built.
Classifying Time-Series Faults with PyTorch
Preprocessing multivariate vibration and acoustic sensor data from a machinery fault simulator, then training an MLP to classify faults — covering downsampling, rolling-window features, t-SNE visualization, and full evaluation metrics.
Building an End-to-End ML Pipeline
A reproducible pipeline for short-term rental price prediction, orchestrated with MLflow, configured with Hydra, and tracked in Weights & Biases — data ingestion through validation, training, and deployment.
Reproduction of the OCBA-MCTS paper — improving Monte Carlo Tree Search through Optimal Computing Budget Allocation, connecting directly to my ranking-and-selection research at Warwick.
More on GitHub, including music genre classification and image description with OpenAI.
Technical Expertise
Languages Python C C++ Bash
ML & Deep Learning PyTorch Hugging Face scikit-learn XGBoost
LLM & Agentic Frameworks LangGraph LangChain Google ADK Gemini Live API OpenAI API Anthropic Claude
LLM Training & Alignment SFT QLoRA Unsloth
Observability & Evaluation LangSmith Langfuse OpenTelemetry Grafana
MLOps & Deployment Docker Kubernetes MLflow FastAPI CI/CD Git
Cloud AWS — SageMaker, ECR, Lambda GCP — Cloud Run, Vertex AI
Data & Pipelines PostgreSQL Redshift InfluxDB
Embedded ARM Cortex-M Jetson Nano Arduino FreeRTOS MQTT
Optimization Bayesian Optimization Multi-objective Evolutionary Algorithms
Research
University of Warwick, Warwick Business School
Doctoral Research
Ranking & selection, Bayesian optimization, and reinforcement learning. Investigated sample-efficient policy learning for constrained supply chain scheduling of biopharmaceutical products, developing Multi-Objective MCTS using marginal hypervolume as the selection criterion. Under Prof. Juergen Branke, collaborated with Prof. Chun-Hung Chen on two-stage ranking-and-selection, extending OCBA and Expected Value of Information algorithms.
Shinshu University
MSc Research
Developed a predictive model approximating complex decision space geometries caused by difficult Pareto set topologies, significantly improving MOEA solution distribution and convergence. Collaborated across the International Associated Laboratory LIA-MODO (Shinshu, Inria France, UAM Mexico), culminating in multiple publications and the Best Student Paper Award at EMO 2019.
Education
Shinshu University
MEng, Electronics and Information Systems
MEXT Monbukagakusho Scholarship (Japanese Government)
Concordia University
Exchange Student, Electrical Engineering
Science Without Borders Scholarship (Brazilian Government)
Federal University of Technology — Paraná
BEng, Electronics
Publications & Awards
🏆 Best Student Paper Award — EMO 2019 · Young Researcher Award — JPNSEC 2018 · Young Research Paper Award — IEICE Shin-etsu 2018
- Y. Marca, H. Aguirre, S. Zapotecas, A. Liefooghe, B. Derbel, S. Verel, K. Tanaka. Approximating Pareto set topology by cubic interpolation on bi-objective problems. EMO 2019, LNCS, Michigan, USA. (Best Student Paper Award)
- Y. Marca et al. MOEA with Cubic Interpolation on Bi-objective Problems with Difficult Pareto Set Topology. JPNSEC Journal, 2019. doi:10.11394/tjpnsec.10.12
- Y. Marca et al. NSGA-II with Spline Interpolation on Bi-objective Problems with Difficult Pareto Set Topology. JPNSEC Symposium on Evolutionary Computation, Fukuoka, 2018. (Young Research Award)
- Y. Marca et al. MOEAs on Problems with Difficult Pareto Set Topologies. IEICE Shin-etsu Branch IEEE Session, Niigata University, 2018. (Young Research Award)
- Y. Marca et al. Pareto dominance-based MOEAs on problems with difficult Pareto set topologies. GECCO ’18 Companion, ACM, 189–190. doi:10.1145/3205651.3205746
Full list on Google Scholar and Lattes.
Languages
Portuguese — Native English — Fluent Japanese — Conversational
Get in touch
The fastest way to reach me is email or LinkedIn. My full CV is available as a PDF.