01HealthTech
IkerCare
Family health tracking, multiple patients, roles, reminders, clinical documents, OCR, encryption, auditing and Android.
Senior AI · Machine Learning · Python · Data Science
I build AI and data systems with real-world impact.
I am Alexis Torres Álvarez, a Machine Learning Engineer, Python developer and AI professional with 12+ years in technology. My background spans government, higher education and private companies, from software engineering to ML, GenAI, RAG, computer vision, NLP, automation and MLOps.
12+years in IT and software engineering
6+years in applied ML / AI
100+Python automations and processes
3open Lab tracks: ML, AI and Quantum
01 · Experience
AI across government, education and the private sector.
I started in enterprise software development and evolved toward data, Machine Learning and production AI while keeping a strong backend and systems integration foundation.
Government · 2020 — present
Ministry of Social Development and Family, Chile
- Machine Learning Engineer / Python & GenAI Developer.
- 100+ Python automations and services supported by CI/CD practices.
- ETL and data pipelines over large datasets, including distributed processing and Databricks.
- NLP for classification and information extraction, computer vision and predictive modeling.
- Internal GenAI assistant using RAG, vector retrieval and workflow orchestration.
- FastAPI, Spark, Dask, Pandas, PyTorch, Transformers/BERT, ResNet50, Oracle, Docker, Kubernetes, Azure/Huawei Cloud, n8n and Flowise.
- Applied data governance, privacy, algorithmic impact assessment, traceability and bias mitigation.
- Institutional participation in inter-ministerial work related to Chile's National AI Policy.
Higher education · 2014 — 2020 + teaching
Universidad de Playa Ancha
- Software engineering, analytics and institutional systems.
- Led SIMES-R, an institutional platform for student monitoring and early-risk detection.
- Predictive student dropout model to support early intervention.
- Systems integration, REST APIs, SQL Server/MySQL and web/desktop applications.
- Later teaching in programming, software development and data science.
- 2026 postgraduate teaching in the MSc in Data Science and Environment, focused on environmental data analysis with ML and Cloud.
Private sector · 2012 — 2014
Artie Consultores & Blue Byte Technologies
- Statistical, financial and back-office systems.
- SAP, SOAP/REST and XML-based integrations.
- HR self-service kiosks with digital signature and biometric validation.
- Systems for casinos, hospitality, POS and clinical management.
- Enterprise software for CSAV and distributed integration with IBM Message Broker.
- Historical stack: .NET, C#, ASP.NET, SQL Server, JavaScript, XML/ESQL, SVN and Team Foundation.
02 · Technical areas
From algorithms to production AI products.
Machine Learning
Regression, classification, trees, ensembles, SVM, KNN, Naive Bayes, clustering, dimensionality reduction, time series and anomalies.
Deep Learning
MLP, CNN, RNN, seq2seq, attention, Transformers, transfer learning and generative models.
NLP, LLMs & RAG
Text classification, BERT/Transformers, embeddings, vector search, agents and retrieval augmented generation.
Image, video & vision
Image classification, ResNet, CNNs, transfer learning, video, tracking and multimodal analysis.
Data Engineering
ETL/ELT, large-scale processing, SQL, Spark, Dask, Databricks and analytics.
AI Engineering & MLOps
APIs, microservices, containers, CI/CD, Kubernetes, cloud, evaluation, monitoring and automation.
03 · Education
Formal programs and topics studied.
The descriptions combine my documented academic path with official institutional curricula. For the MTI, the listed topics correspond to the 2018–2020 period I attended.
Master's Degree in Artificial Intelligence
My current formal graduate program is Artificial Intelligence. Quantum Computing is now presented as an independent study/Lab track, not as my current master's degree.
Core subjects and areas
- Research and Project Management in Artificial Intelligence.
- Natural Language Processing.
- Computer Vision.
- Machine Learning Techniques.
- Automated Reasoning and Planning.
- Neural Networks and Deep Learning.
- Unsupervised Machine Learning.
- Elective / Cloud Computing for AI and Master's Thesis.
- Tools: Python, TensorFlow, scikit-learn, NLTK, Jupyter/Colab, STRIPS/PDDL, Azure and AWS.
Diploma in Artificial Intelligence
Topics
- CNNs and relevant convolutional architectures.
- RNNs, sequence-to-sequence models and attention.
- Transformers and relational networks.
- Activation/loss functions, regularization, optimization, dropout and batch normalization.
- Transfer learning and data augmentation.
- Visual recognition, pretrained models and NLP.
- Graph neural networks, reinforcement learning, imitation and inverse RL.
- GANs, recommendation, incremental learning, self-supervision and meta-learning.
- Video analysis, tracking, audio/speech recognition, audio+video and deepfakes.
- Privacy, bias, ethics and responsibility.
Diploma in Big Data & Machine Learning
Topics
- Big Data, Hadoop, HDFS, YARN, MapReduce and ecosystem tools.
- Spark, RDDs, distributed applications and Spark SQL.
- Python for Data Science: preparation, imputation, transformation and visualization.
- Linear, polynomial, penalized and logistic regression.
- Naive Bayes, decision trees, Random Forest, KNN and classifier evaluation.
- Basic neural networks.
- K-Means, hierarchical clustering, Gaussian mixtures, cluster evaluation and dimensionality reduction.
- Applications across structured data, text, audio and video; supervised, unsupervised and reinforcement learning.
Master in Information Technology
Topics from my cohort
- Enterprise information systems and business processes.
- Decision support, Business Intelligence and decision making.
- UML, analysis/design and software process models: waterfall, iterative, RUP, XP, AUP and Scrum.
- Software process quality models.
- Networks, security, SDN and OpenFlow.
- Project management, leadership, motivation and change management under PMBOK.
- BPM and BPMN.
- Data analysis, Data Warehousing, OLAP, data mining and predictive models.
- Semantic Web and data representation: XML, JSON and RDF.
Computer Engineering
Professional foundation in software engineering, algorithms, programming, databases, systems architecture and information technologies.
04 · Courses and certifications
Continuous learning.
Microsoft Certified: Azure Fundamentals (AZ-900)Microsoft · 2026 · Cloud concepts, Azure architecture/services, management & governance.
Continuing Education Certificate in LeadershipMIU / City University Miami · 2025 · 45 h.
AI for Work: Fundamentals, tools and good practicesFEN · Universidad de Chile · 2025 · 16 h.
Service Design and User ExperienceFEN · Universidad de Chile · 2025 · 16 h.
DevOps Methodology Implementation FundamentalsDUOC-UC · 2024 · 16 h.
Applied Gender TrainingODEGI · 2024 · 16 h.
Collaborative Team ManagementTarget DDI · 2022 · 36 h.
Python Programming Tools for Data ProcessingPontificia Universidad Católica de Chile · 2020 · 30 h.
English Language CourseBritish Council English · 2023 — 2024 · 98 h.
English Lessons with TutorsPreply · 2023 · 28 h.
English A2ECLASS · 2017 · 180 h.
English A1ECLASS · 2016 · 180 h.
Academic recognition: CONICYT/ANID public-sector master's scholarship linked to the Master in Information Technology.
05 · Open Labs
Learn by building, in progressive order.
Each track contains runnable Google Colab notebooks, exercises and a historical/conceptual progression. The repositories are public and designed as technical portfolio plus educational material.
Machine Learning & Data Science
From baselines and regression to ensembles, unsupervised learning, neural nets, time series and MLOps.
Artificial Intelligence
Symbolic AI → deep learning → image/NLP/audio/video → planning → LLM/RAG → RL, graphs, multimodal and Responsible AI.
00 · History, search & agentsColab ↗01 · Logic & expert systemsColab ↗02 · Deep LearningColab ↗03 · Vision & imageColab ↗04 · NLP & TransformersColab ↗05 · Audio & speechColab ↗06 · Video & trackingColab ↗07 · Reasoning & planningColab ↗08 · LLM, embeddings & RAGColab ↗09 · Reinforcement learning & graphsColab ↗10 · Multimodal GenAI & Responsible AIColab ↗
Quantum Computing
Independent study track: math, qubits, circuits, entanglement, protocols, algorithms, NISQ, noise and Quantum ML. It is not presented as my current master's degree.
06 · Teaching & outreach
Educational experience.
MSc Data Science & Environment · UPLA
2026 postgraduate teaching on environmental data analysis with Machine Learning and Cloud.
Business Engineering · UTFSM
2022 · Data processing, advanced Excel, forms, VBA macros and Python.
Computer Engineering · UPLA
2021 · Structured Programming in C++, algorithms and problem solving.
TIC technical program · UPLA
2016 — 2018 · Software development methodology, development workshop, internships and capstone supervision/evaluation.
Summer School 2026 · UPLA
Speaker on data and public decision-making, governance, privacy and regulation in the AI era.
Professional community
Participation in ILIA/CEPAL, Responsible AI seminars, Data Governance events, Huawei Cloud Summit and other activities.
07 · Projects
Products and experiments.
This remains separate from Labs: Labs teach concepts; projects are complete products. Each project will be expanded into a case study in the next iteration.
02GovTech
GovLens
Project focused on public-information exploration and transparency using data and AI tools.
03EdTech
Interactive Tech Notes
Interactive HTML summaries and visual learning resources about AI, Machine Learning and software development.
08 · Contact
Applied AI, products, education and technical collaboration.
Open to AI Engineering, Machine Learning, Data Science, GenAI/RAG, automation, Python architecture, teaching and technology initiatives.