Software architecture and implementation
Design and delivery of maintainable backend services with clear boundaries, clean APIs, integration paths, testing, and practical ownership models.
Technical consulting
Reliable software systems for technical teams.
Backend, cloud, and integration consulting for teams that need maintainable APIs, production-ready platforms, and software engineering discipline across complex domains.
Consulting Areas
Software engineering support for systems that need to hold up.
Practical consulting for teams that need clear architecture, maintainable implementation, cloud-native delivery, and disciplined engineering workflows.
Cloud-native delivery
Containerized services with Docker, Kubernetes, Helm, CI/CD automation, release discipline, and deployment practices that reduce operational uncertainty.
Backend systems across domains
Engineering support for product, platform, research, and integration-heavy environments where reliability, maintainability, and delivery quality matter.
Telecom-grade specialization
Standards-aware backend work for 5G Core concepts, network exposure, CAMARA-style open network APIs, B5G/6G experimentation, and reproducible test platforms.
AI-assisted engineering workflows
Practical use of AI coding tools and agentic workflows for planning, implementation, test generation, refactoring, documentation, and review while keeping engineering judgment in control.
Proof Areas
Experience-backed capabilities across software systems.
Experience is grouped by technical capability instead of company names or a chronological resume. Telecom is a specialization, not the boundary of the work.
Telecom-grade backend systems
Backend engineering for network-function platforms where software quality, deployment control, and clear integration contracts matter.
Experience covers 5G Core concepts, signaling-oriented backend services, Java and RxJava, Cassandra, YANG models, Docker, Kubernetes, Helm, Jenkins, CI/CD, automated tests, and customer-facing technical documentation.
Open network API platforms
API and platform work for network experimentation, standards-oriented integration, and reproducible technical workflows.
Work includes FastAPI services, Pytest-based workflows, Docker and docker-compose environments, CAMARA-style open network API concepts, Open5GS and PacketRusher experiments, Network Exposure Function concepts, CAPIF concepts, and technical review of 3GPP-oriented specifications.
Backend modernization and quality
Modernization and maintenance of software systems in environments where quality, integration testing, and deployment control are important.
Experience includes Spring Boot backend services, Java EE application maintenance, Spring Test coverage, integration tests, deployment support, quality feedback loops, and reliability work around security-sensitive software.
Engineering delivery acceleration
Use of modern AI-assisted and agentic coding workflows as a software delivery multiplier, not as a replacement for engineering discipline.
Applied carefully, AI-assisted workflows can shorten feedback loops for implementation planning, code exploration, refactoring, test generation, documentation, and review while keeping architecture, security, and maintainability explicit.
How I Help
From architecture and prototypes to deployable software.
Engagements can be narrow and implementation-heavy or broader across architecture, delivery workflows, and integration constraints.
Move prototypes toward production
Clarify service boundaries, make APIs testable, containerize the runtime, and turn promising technical work into deployable software.
Unblock backend and platform delivery
Support implementation, integration, CI/CD hardening, Kubernetes packaging, and technical execution alongside existing engineering teams.
Support standards-aware technical work
Bring practical software engineering to standards-aware, research-oriented, and platform contexts where specifications and integration constraints matter.
Shorten feedback loops with AI-assisted delivery
Apply AI-assisted development where it helps: technical exploration, implementation planning, test coverage, refactoring, documentation, and review.
Tech Stack
Tools and practices for software engineering delivery.
A backend-first stack with cloud-native delivery, integration depth, automated quality checks, telecom specialization, and AI-assisted engineering workflows used carefully as a delivery multiplier.
Backend
- Java
- RxJava
- Spring Boot
- Python
- FastAPI
- Java EE
Cloud and platform
- Docker
- Kubernetes
- Helm
- docker-compose
- Linux
Data and integration
- Cassandra
- MongoDB
- YANG models
- REST/HTTP
- Microservices
Delivery and quality
- Jenkins
- CI/CD
- Pytest
- Spring Test
- JUnit
- TestNG
Telecom specialization
- 5G Core
- CAMARA
- NEF
- CAPIF
- Open5GS
- PacketRusher
- B5G/6G
Engineering workflow
- AI-assisted development
- Agentic coding workflows
- Code review
- Test generation
- Documentation
Credentials
Signals behind the consulting work.
Formal engineering background, Kubernetes certification, network systems research, and publication topics that support practical software engineering and platform work.
Certified Kubernetes Application Developer
Kubernetes application design, deployment, configuration, observability, and troubleshooting foundations.
Integrated Master in Electrical and Computer Engineering
Engineering background across computer systems, networks, software, and telecommunications.
Software Defined Networks thesis
Thesis work on ONOS controller compatibility with the P4 programming language for Software Defined Networks.
Publication topics
Peer-reviewed work around CAMARA-style open network APIs, 3GPP NEF concepts, location reporting, network openness, and telecom experimentation.
Consultant Profile
Backend and cloud engineering with implementation depth.
Panagiotis Pavlidis helps technical teams clarify architecture, implement reliable services, improve deployment models, and move complex backend work toward software that can be tested, shipped, and operated.
The focus is backend and cloud delivery with telecom-grade systems context: service boundaries, integration quality, Kubernetes packaging, CI/CD feedback loops, standards-aware implementation, and pragmatic use of AI-assisted development practices.
- 01 Software systems with clear contracts and maintainable boundaries.
- 02 Cloud-native delivery using containers, Kubernetes, Helm, and CI/CD.
- 03 AI-assisted development used to improve delivery, testing, review, and documentation.
Contact
Bring a software, backend, cloud, or delivery challenge.
Share the system context, constraints, and delivery goal. I help technical teams move from architecture and prototypes to reliable, deployable software.