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Final-year MSc student in Computer Science and Engineering at IST – University of Lisbon, specializing in Data Science and Artificial Intelligence, currently completing my thesis at NTNU. I have earned academic merit awards for both my BSc and MSc, and I bring experience across internships, research, and leadership roles. I am seeking a full-time position in Oslo. Curious, proactive, and community-driven, I thrive in collaborative environments and enjoy turning complex ideas into well-executed, high-impact projects.…Final-year MSc student in Computer Science and Engineering at IST – University of Lisbon, specializing in Data Science and Artificial Intelligence, currently completing my thesis at NTNU. I have earned academic merit awards for both my BSc and MSc, and I bring experience across internships, research, and leadership roles. I am seeking a full-time position in Oslo. Curious, proactive, and community-driven, I thrive in collaborative environments and enjoy turning complex ideas into well-executed, high-impact projects.

Inês Trigueiro

AI Engineer, Data Scientist, Developer, +4

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Final-year MSc student in Computer Science and Engineering at IST – University of Lisbon, specializing in Data Science and Artificial Intelligence, currently completing my thesis at NTNU. I have earned academic merit awards for both my BSc and MSc, and I bring experience across internships, research, and leadership roles. I am seeking a full-time position in Oslo. Curious, proactive, and community-driven, I thrive in collaborative environments and enjoy turning complex ideas into well-executed, high-impact projects.…Final-year MSc student in Computer Science and Engineering at IST – University of Lisbon, specializing in Data Science and Artificial Intelligence, currently completing my thesis at NTNU. I have earned academic merit awards for both my BSc and MSc, and I bring experience across internships, research, and leadership roles. I am seeking a full-time position in Oslo. Curious, proactive, and community-driven, I thrive in collaborative environments and enjoy turning complex ideas into well-executed, high-impact projects.

Available to hire

Final-year MSc student in Computer Science and Engineering at IST – University of Lisbon, specializing in Data Science and Artificial Intelligence, currently completing my thesis at NTNU. I have earned academic merit awards for both my BSc and MSc, and I bring experience across internships, research, and leadership roles. I am seeking a full-time position in Oslo.

Curious, proactive, and community-driven, I thrive in collaborative environments and enjoy turning complex ideas into well-executed, high-impact projects.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

Portuguese

Fluent

English

Fluent

Work Experience

AI Engineer Intern at Deloitte

May 1, 2025 - July 31, 2025

Designed and deployed a production-grade GenAI multi-agent system for Luz Saúde, in partnership with Google, built on Google Cloud Platform. Architected agentic workflows using Google ADK in Python, enabling tool use, RAG, embedding models, information retrieval pipelines, real-time A2A streaming, structured outputs, and self-evaluation with RAGAS metrics. Performed prompt engineering and hyperparameter tuning for LLM-based agents to optimize accuracy, faithfulness, and clinical reliability. Containerized services with Docker, provisioned infrastructure using Terraform, managed dependencies with Poetry, and developed REST APIs using FastAPI; frontend built with OutSystems. Managed GCP IAM permissions, used Azure Repos for version control, and worked in an Agile setting, gathering requirements and co-presenting the final demo. Return offer received.

AI Engineer at Cogito NTNU

August 1, 2025 - Present

Joined a five-person team to build RegulAId, a full-stack, AI-powered compliance assistant for the EU AI Act. Implemented a production-ready RAG chatbot with cited answers, achieving eightfold higher RAGAS faithfulness versus a baseline. Designed a hybrid retrieval pipeline combining BM25 keyword search with vector embeddings and Reciprocal Rank Fusion, backed by MongoDB for semantic search. Developed agentic workflows in Python using LangChain and LangGraph; integrated LangSmith for observability; produced structured outputs with article-level citations. Implemented automated risk classification and compliance checklist generation aligned with the EU AI Act’s four-tier risk framework, leveraging GPT-4 for reasoning. Dockerized services, containerization and environment management with virtualenv; CI/CD with GitHub Actions; version control on GitHub; optimized data preprocessing to improve retrieval accuracy and system reliability.

Board Member and Project Lead at ReStore

August 1, 2025 - Present

As Board Member and Project Lead for ReStore (a 150-strong volunteer organization focused on reuse and sustainability), I lead the Outreach Team and the ReBuild project. I coordinate weekly meetings, set goals, distribute responsibilities, and empower volunteers. Through structured social media strategy and campus outreach, I doubled participation in volunteering sessions and strengthened organizational visibility. I also lead cross-team collaboration to repurpose furniture for students, contributing to ReStore’s impact, including saving 838+ tons of CO2 since 2019.

ML Researcher at INESC-ID

February 1, 2024 - July 1, 2024

ML Researcher who developed a high-performance cancer classification pipeline using proteomic expression data, achieving >97% F1-score across 42 cancer types. Designed end-to-end data preprocessing and benchmarked multiple models with scikit-learn and PyTorch, using cross-validation and Optuna for hyperparameter optimization, and an evaluation-driven model selection framework. Improved computational efficiency by parallelizing training and managing structured datasets with PostgreSQL and Pandas. Received return offer.

ML Researcher at INESC - ID

February 1, 2024 - July 1, 2024

Developed a high-performance cancer classification pipeline using proteomic expression data; achieved >97% F1-score across 42 cancer types. Designed end-to-end data preprocessing workflows and benchmarked multiple ML models with scikit-learn and PyTorch; applied cross-validation and Optuna for hyperparameter optimization; parallelized training; managed structured datasets with PostgreSQL and Pandas. Received return offer.