Sara Nóbrega
I work on building practical, production-ready AI systems.
End-to-end delivery from data → model → deployment.
My Approach to AI Innovation
My work focuses on turning AI ideas into production-ready systems that solve concrete business problems.
I start with clear success metrics, iterate through weekly demos, and only ship what can run reliably in production.
I start with clear success metrics, iterate through weekly demos, and only ship what can run reliably in production.
Success Stories
Logistics Optimization
- Problem: Models needed to be deployed, monitored, and maintained reliably
- What was built:
- ML services deployed via APIs
- CI/CD pipelines for training and deployment
- Monitoring and experiment tracking
- Outcome:
- Reduced manual deployment
- Improved reliability and reproducibility
- Clear ownership of ML lifecycle
Automating Internal & External Collaboration with AI
- Problem: Manual coordination across files, emails, and collaborators
- Solution:
- Automated workflows for asset sharing
- AI-assisted email handling
- Clear handoff processes for teams
- Outcome:
- Less manual coordination
- Faster turnaround
- Clear documentation for scaling
LLM-Powered AI Agent for Knowledge & Communication
Problem:
Teams struggled to keep up with internal communication, documentation, and context spread across emails, Slack, and tools.
Teams struggled to keep up with internal communication, documentation, and context spread across emails, Slack, and tools.
What was built:
- AI agents for summarizing conversations and documents
- Automated documentation generation from ongoing workflows
- Integrations with Slack and internal tools to surface insights where teams work
Outcome:
- Reduced information overload
- Faster onboarding and knowledge transfer
- Consistent documentation without manual effort
Workflow Automation for Business Operations
Problem:
Manual processes across sales, scheduling, and coordination created bottlenecks and missed follow-ups.
Manual processes across sales, scheduling, and coordination created bottlenecks and missed follow-ups.
What I built:
- Automated lead qualification and routing
- Email and calendar workflows triggered by business events
- End-to-end automation using low-code tools where appropriate
Outcome:
- Fewer manual steps and errors
- Faster response times
- Scalable processes without additional headcount
Testimonials
What clients say after working with me on AI systems and automation.
Where I Have Delivered Value
Logistics & Operations (GLS)
- Built and deployed production AI services end-to-end (data → model → API → monitoring)
- Designed CI/CD pipelines for ML models, reducing manual deployment risk
- Integrated ML systems with Azure infrastructure and Snowflake-based pipelines
Energy & R&D (EDP)
- Developed forecasting and synthetic data solutions for energy efficiency projects
- Shipped ML models supporting EU-funded initiatives with real-world constraints
- Balanced research depth with deployment feasibility
Startups & Early-Stage Products (Workist, Irdeto)
- Implemented NLP and CV systems (BERT, Faster-RCNN) in real product contexts
- Worked across experimentation, evaluation, and production handoff
- Learned how to ship under ambiguity and tight timelines
Background & Foundations
MSc in Physic with Specialization in Data Science
Studied advanced statistics, machine learning, and big data technologies.
BSc in Physics
Specialized in data structures, algorithms, and software development.
Insights from Building ML Systems
A practical walkthrough of building an AI workflow that automatically prioritizes and routes emails, reducing manual triage and operational overhead.
Explores how large language models can augment traditional time-series workflows, supporting faster analysis, debugging, and insight generation.
Shows how to design, test, and iterate prompts that remain reliable across data, edge cases, and evolving project requirements.