I take GenAI from concept to production, building architectures that balance response quality, operational cost, observability, and compliance. I work with solutions that connect data, models, and automation to support strategic decisions and continuous innovation.
#01
Ingestion & Processing
Databricks, Spark and scalable data pipelines
#02
Intelligence & Context
GenAI, LLMs, RAG and retrieval systems
#03
Architecture & Governance
Lakehouse, data modeling and governance
#04
Operations & Automation
DataOps, MLOps and AI systems observability
#05
Infrastructure & Cloud
Azure and AWS for data and GenAI
I'm Alexsander Valente, Data & AI Engineer specialized in GenAI, with over ten years of experience in technology, building data and artificial intelligence systems for production. My background in software engineering shaped a solid vision of architecture, scalability, and code quality, now applied to data and GenAI.
I design and develop modern data architectures, scalable pipelines, and GenAI-based solutions, integrating data engineering, language models, and automation to deliver reliable and efficient solutions.
In recent years, I've deepened my focus on LLMs, RAG and Lakehouse architectures, working in regulated and high-criticality environments, always focused on taking AI from concept to production, balancing response quality, cost, observability, and compliance.
I design integrated cloud ecosystems using Azure, AWS, and GCP, with the goal of transforming data and GenAI into real decision-making capability and measurable business impact.
Specialized skills refined through years of delivering high-impact solutions
#01
Designing and orchestrating high-performance enterprise pipelines, processing over 100 million records per day with a focus on scalability, governance, and reliability.
Advanced experience in Databricks, Apache Spark, Airflow, Kafka, dbt, and Delta Lake, applying the Medallion pattern (Bronze, Silver, Gold) for modern Lakehouse architectures.
#02
Developing and deploying production AI solutions, combining LLMs, RAG Systems, and NLP to create intelligent and contextual applications.
Hands-on experience with MLflow, LangChain, CrewAI, and OpenAI API, applying robust MLOps practices to ensure scale, versioning, and continuous monitoring.
#03
Designing and migrating cloud-native architectures on Azure, AWS, and GCP, with a focus on infrastructure as code, scalability, and observability.
Proficiency in Docker, Kubernetes, and CI/CD pipelines, automating deployments and ensuring high availability and continuous performance.
#04
Modern full-stack development with Python, Node.js, and TypeScript, creating scalable REST APIs, microservices, and high-performance web applications.
Solid experience with Django, FastAPI, React, and Next.js, applying best practices in architecture and automated testing.
Complete documentation kit for enterprise-level Generative AI projects, bringing together reference architectures, technical patterns, essential checklists, and support materials for production-ready solutions.
Comprehensive patterns for RAG, Agents, LangGraph, and enterprise security.
Interactive validation with 170+ items. Export to Markdown.
50+ terms with definitions, categories, and related concepts.
8 specialized templates for GenAI architecture decisions.
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I'm open to new opportunities and partnerships in Data Engineering, AI/ML and MLOps. Get in touch and let's explore together how I can add value to your team and deliver high-impact results.
Get in touch and let's discuss how I can contribute.