AI Product Integration
Bringing AI capabilities into enterprise products by connecting language models, backend services, internal systems, and business workflows.
Hi, I'm
Bernardo Alves
Software Engineer | AI Engineering & Technical Leadership
Bringing AI into enterprise products, transforming software delivery, and leading technical initiatives from strategy to infrastructure.
AI Engineering & Technical Leadership
Driving the adoption of AI across enterprise software products and the software development lifecycle.
My work goes beyond implementing AI-powered features. I help shape how AI is integrated into products, how engineering teams use AI throughout the SDLC, and how the supporting systems and infrastructure evolve to enable these capabilities.
My responsibilities span AI strategy, technical direction, internal system integrations, application development, cloud infrastructure, and continuous process optimisation.
SDLC Transformation
Defining and evolving AI adoption practices across the software development lifecycle, from requirements and implementation to testing, code review, documentation, and delivery.
Technical Leadership
Driving technical initiatives from architectural decisions through implementation and infrastructure, aligning engineering solutions with product and business objectives.
Engineering Process Optimisation
Identifying opportunities to reduce repetitive work, improve development workflows, and use AI to increase engineering effectiveness without compromising quality or reliability.
Enterprise Integration & Infrastructure
Connecting AI capabilities to internal enterprise systems and building the application, backend, and cloud foundations needed to support them.
Services
What I build and enable through software engineering, AI, and cloud technologies.
AI Engineering
AI-powered applications and intelligent workflows that solve real business problems. From LLM integrations and prompt engineering to document processing, structured data extraction, and AI-assisted automation.
AI Integration & Automation
Integrating AI capabilities into existing products and business processes. Designing workflows that combine language models, APIs, validation, and traditional software systems to reduce repetitive work and improve efficiency.
Websites & Web Applications
Custom, responsive web experiences and applications built around business requirements, usability, performance, and maintainability.
Cloud & Backend Engineering
Scalable backend services, API integrations, and cloud infrastructure that connect applications, data, and AI capabilities.
Digital Branding
Professional digital identities and online experiences that help businesses build trust and stand out.
Technical Consulting
Engineering improvements, architecture decisions, AI adoption strategies, and development workflows that help teams deliver better software.
SEO & Google
Search visibility and Google Business setup so customers find you first.
Ads
Targeted ad campaigns focused on bringing in new customers, not just clicks.
Maintenance
Ongoing support and updates, so the site keeps working and evolving after launch.
Packages
Launch
A professional digital presence or focused software solution, designed and delivered around a clearly defined business need.
Growth
A connected digital experience combining web development, automation, analytics, and growth-focused improvements.
Partner
An ongoing engineering partnership covering maintenance, AI integration, automation, and continuous technical improvement.
Trusted by
Launch package · in progress
About
I'm Bernardo Alves, a Software Engineer at Kuehne+Nagel working at the intersection of AI engineering, enterprise software, cloud technologies, and technical leadership.
My background spans Android development, Kotlin, backend engineering, cloud integration, and observability. Today, my focus is expanding towards AI-enabled products and the transformation of software engineering practices.
I'm involved in a global initiative where I help bring AI into the product, connect AI capabilities with internal systems, optimise workflows, and shape the strategy for AI adoption across the software development lifecycle.
My responsibilities extend across technical direction, application and backend development, infrastructure, and the evolution of engineering practices. I approach AI not as an isolated feature or coding assistant, but as a capability that needs to be integrated into real systems, supported by sound architecture, reliability, and clear business objectives.
I'm particularly interested in making AI useful at scale: turning experimentation into practical solutions, helping teams work more effectively, and building the technical foundations for sustainable AI adoption.
My long-term direction is to combine hands-on engineering expertise with technical leadership, helping teams and organisations translate emerging technologies into meaningful business outcomes.
AI & Engineering Expertise
A practical engineering toolkit spanning AI, software architecture, cloud infrastructure, and modern development workflows.
Enterprise AI integration, LLM workflows, SDLC transformation, AI adoption strategy and process optimisation.
Enterprise AI integration LLM workflows and prompt engineering Structured outputs and human-in-the-loop validation SDLC transformation AI adoption strategy Process optimisation AI-assisted development (Claude Code, OpenCode CLI, GitHub Copilot, Gemini)
Featured Work
A selection of engineering initiatives spanning application modernisation, AI integration, observability, and cloud-connected systems.
Featured project
AI-Powered Operations Dashboard
AI Engineering · Technical Leadership · Enterprise Integration
- Business context
- A global operations organisation running at very high processing volumes. At this scale, improving operational workflows and enabling effective use of AI can create meaningful opportunities across the business.
- My role
- Driving AI adoption within a global operational product, combining technical leadership, AI integration, software development, and infrastructure.
- Scope of responsibility
- AI strategy: Shaping and evolving how AI is adopted across the product and software development lifecycle.
- Product integration: Bringing AI capabilities into operational workflows and integrating them with internal enterprise systems.
- Technical leadership: Leading technical initiatives across application development, backend services, integrations, and infrastructure.
- SDLC optimisation: Exploring and implementing AI-enabled practices that improve the way software is designed, developed, tested, and delivered.
- Engineering foundations: Helping establish scalable, reusable approaches to AI integration and future development initiatives.
- Engineering focus
- Large language models, AI workflows, enterprise APIs, backend and web application development, and cloud infrastructure.
- Objective
- Build a sustainable approach to enterprise AI adoption that improves product capabilities, streamlines workflows, and evolves engineering practices beyond individual AI experiments.
AI-Powered Document Processing
- Business challenge
- Business workflows often rely on documents in different formats, making information extraction, transformation, and validation time-consuming and difficult to standardise.
- Approach
- Contributing to an AI-assisted workflow designed to process documents and transform their contents into structured outputs, combining language models with backend services.
- Technologies
- Amazon Bedrock, Claude models, Spring Boot, AWS, Angular.
- Engineering focus
- AI-powered document understanding
- Prompt design and structured data generation
- Backend workflow orchestration
- Integration between AI services and web applications
- Validation and reliable output handling
- Objective
- Reduce manual processing and create a more scalable approach to transforming business documents into structured data.
AI-Assisted Software Development
- Business challenge
- Engineering teams need to improve development efficiency without sacrificing code quality, maintainability, or sound technical judgment.
- Approach
- Promoting the practical adoption of AI-assisted development tools and workflows within the engineering process.
- Technologies
- GitHub Copilot, AI-assisted coding workflows.
- Impact
- Supporting more efficient development practices while keeping engineering standards, code review, and developer ownership central to delivery.
Android Application Modernisation
- Business challenge
- A mature logistics application relied on legacy Java and MVVM patterns that needed modernisation.
- Approach
- Contributing to the transition towards Kotlin, MVI, and modern Android development practices.
- Technologies
- Kotlin, Java, Android, MVI, Jetpack Compose, Hilt.
- Impact
- Improving architectural consistency and maintainability while establishing a stronger foundation for future development.
Observability & Production Insights
- Business challenge
- Limited visibility into application behaviour makes production diagnosis and operational improvement more difficult.
- Approach
- Introducing standardised telemetry and observability practices across the application ecosystem.
- Technologies
- OpenTelemetry, Grafana, Loki, Tempo, Mimir.
- Impact
- Improving visibility into system behaviour and strengthening the team's ability to investigate operational issues.
Cloud & Backend Integration
- Business challenge
- Modern applications need reliable communication between mobile clients, backend services, and cloud infrastructure.
- Approach
- Working across application and backend boundaries to support integrations and new capabilities.
- Technologies
- AWS, Spring Boot, Kotlin, REST APIs.
- Impact
- Strengthening the connection between mobile applications, backend services, and cloud-based capabilities.
Engineering Philosophy
The principles that shape how I approach engineering work.
AI With Purpose
Use AI to solve meaningful problems, not simply because a technology is available. Start with the business need and measure the value delivered.
Engineering Fundamentals First
AI-generated output still requires sound architecture, testing, validation, and engineering judgment.
Ownership
Take responsibility beyond implementation, considering maintainability, operational impact, and business outcomes.
Automation
Eliminate repetitive work and design workflows that let people focus on tasks where human judgment matters most.
Reliability & Observability
Build systems that can be monitored, understood, tested, and improved after deployment.
Continuous Learning
Explore emerging technologies and turn experimentation into practical engineering capabilities.
Career
A professional path from Android development toward broader software engineering and technical leadership.
Software Engineer
Kuehne+Nagel
November 2023 – Present · Porto, Portugal
Working on logistics and freight management software, leading technical initiatives across AI integration, application and backend development, and cloud infrastructure. Background in Android modernisation, Kotlin-based backend development, Angular, and observability, with a focus on engineering process optimisation and cross-functional technical collaboration.
Mobile Developer
Xpand IT
December 2021 – October 2023 · Viana do Castelo, Portugal
Worked on financial services projects for BNP Paribas and Consors Finanz, spanning backend development, Android applications using Kotlin and Java, and iOS development.
Vice President
NEEI – Núcleo de Estudantes de Engenharia Informática do IPVC
September 2019 – November 2020 · Viana do Castelo, Portugal
Student association leadership experience that helped shape my approach to collaboration, responsibility, and technical leadership.
Bachelor's Degree in Computer Science
Instituto Politécnico de Viana do Castelo
Currently Exploring
Where I am directing my learning right now.
- AI EngineeringLLM applications, orchestration, structured outputs, and evaluation.
- AWS & Generative AICloud-native AI integrations and production-oriented workflows.
- Software ArchitectureReliable, maintainable systems across mobile, backend, and AI.
- Technical LeadershipTechnical direction, mentoring, collaboration, and engineering practices.
- AI-Enabled EngineeringImproving software delivery through intelligent tools and automation.
- Engineering ManagementTeam organisation, agile ways of working, and delivery practices.
Contact
Let's build something intelligent.
Open to conversations about AI engineering, software architecture, cloud technologies, automation, and technical leadership.
Email: bjca1998@gmail.com