Daniel Fernandez-Guarda
Verified Expert in Engineering
ML Engineer and Developer
Baie-des-Sables, Canada
Toptal member since June 19, 2026
Daniel is a senior ML engineer with 6+ years of experience in agentic AI architecture and model research for clients including Camunda, Datavalet, and Elsevier. His primary expertise is in reinforcement learning, retrieval-augmented generation (RAG), and time-series forecasting for large-scale production environments, where he thrives in high-throughput and autonomous decision-making systems. Daniel has achieved a 10x improvement in workflow efficiency while at Camunda.
Portfolio
Experience
- Statistics - 8 years
- Computer Science - 8 years
- XGBoost - 6 years
- Vertex AI - 6 years
- Reinforcement Learning - 6 years
- SQL - 6 years
- Deep Learning - 6 years
- Python - 6 years
Preferred Environment
Docker, Kubernetes, Kafka, Spark, PySpark, Databricks, Vertex AI, Camunda BPM
The most amazing...
...pipeline I've built processed 1+ billion data points daily with 84% predictive accuracy.
Work Experience
ML Engineer
Camunda
- Engineered and delivered agentic AI demos for enterprise customers, achieving 10x improvements in workflow efficiency and defining the long-term AI direction for the core product.
- Designed and deployed local AI inference pipelines utilizing reinforcement learning to refine model weights.
- Improved autonomous decision-making accuracy by 25% while significantly reducing API latency.
- Spearheaded the development of advanced Copilot functionalities.
- Optimized complex prompts to drive intelligent, context-aware automation.
Data Scientist
Datavalet
- Developed and deployed LSTM Autoencoder models using BigQuery ML to detect network anomalies across 1,300+ sites.
- Reduced manual troubleshooting by 600+ person-hours annually through automated anomaly detection systems.
- Architected production workflows using Kafka and Vertex AI, processing 1+ billion data points daily with a verified 84% predictive accuracy.
- Enabled high-reliability pipelines that were instrumental in securing multi-million dollar enterprise deals.
- Integrated RAG systems for proactive network management.
- Saved hundreds of hours in debugging through contextual data retrieval.
Data Scientist
Elsevier
- Automated Databricks development workflows using PySpark and Scala.
- Optimized processing time from 100 hours down to 4 hours (96% speed increase) while saving 40 person-hours monthly in maintenance.
- Engineered a TF-IDF-based classifier that reduced computation time and operational costs by 90%.
Data Scientist
Laplace Insights
- Led research for integrating Meta's Prophet and FFORMA models using XGBoost and random forest into R-Studio pipelines.
- Achieved 100x training data throughput via parallel scalability.
- Predicted 100+ financial time series daily for portfolio optimization.
Experience
RTLS Data Derandomization Using GraphSAGE
Education
Bachelor's Degree in Mathematics
University of Sherbrooke - Québec, Canada
Skills
Libraries/APIs
XGBoost, PyTorch, TensorFlow, PySpark
Tools
Git, Microsoft Copilot, Prophet ERP, Camunda BPM, Open Neural Network Exchange (ONNX)
Languages
Python, R, Java, Scala, SQL
Frameworks
Apache Spark, Spark, Hadoop
Platforms
Kubeflow, Google Cloud Platform (GCP), Vertex AI, Databricks, Docker, Kubernetes
Other
Large Language Models (LLMs), Retrieval-augmented Generation (RAG), Copilot Architecture, Deep Learning, Machine Learning, Trading, Natural Language Processing (NLP), Data Science, Algorithms, Machine Learning Operations (MLOps), Model Deployment, Model Evaluation, Model Monitoring, Statistical Modeling, CI/CD Pipelines, Model Tuning, Personalization, Recommendation Systems, GraphSAGE, Reinforcement Learning, Prompt Engineering, Kafka, Tf-idf, R-Studio, Statistics, Computer Science, GRAPH, APIs, Quantization
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