Senior Backend Engineer | Java & Python | Software Architecture & Applied AI
Java & Spring Boot · Python & FastAPI · System Design · Cloud-Native Delivery · Applied AI
I'm a Senior Backend Engineer with 10+ years of experience designing, modernising and delivering backend systems in complex enterprise environments. Java/Spring Boot is my primary backend stack. During the last three years, I have also built backend services, automation and applied AI workflows with Python/FastAPI.
My core expertise includes:
- ☕ Java 21+ and Spring Boot microservices, REST APIs and backend platforms
- 🐍 Python/FastAPI for backend services, automation, tooling and applied AI workflows
- 🧱 Domain-Driven Design, Hexagonal Architecture and event-driven systems
- 📨 Kafka and reliable synchronous/asynchronous integrations
- ☁️ Cloud-native backend delivery with GCP, AWS, Kubernetes and Docker
- 🧭 Architecture decisions, RFCs/ADRs, mentoring and code reviews
- 🤖 Applied AI integration with LLM-backed services, LangGraph, RAG and tool orchestration
I help teams make sound architectural decisions without stepping away from implementation, reviews or delivery.
| Area | Tools & Technologies |
|---|---|
| Languages | Java 21+ (primary), Python (3 years in backend, automation and applied AI), Kotlin (JVM ecosystem exposure) |
| Backend | Spring Boot, Spring Cloud, Spring Security, FastAPI, REST APIs |
| Architecture | DDD, Hexagonal Architecture, Clean Architecture, Microservices, Event-Driven Systems |
| Data & Messaging | Kafka, PostgreSQL, MongoDB, Redis |
| Cloud & Platform | GCP/GKE, AWS, Kubernetes, Docker, CI/CD |
| Quality & Testing | TDD, JUnit 5, Mockito, AssertJ, Testcontainers, WireMock |
| AI & Integration | LLM Integration, RAG, LangGraph, MCP, Tool Orchestration |
| Observability & Security | Grafana, Prometheus, CloudWatch, OAuth 2.0, JWT |
| Architecture Docs | OpenAPI, C4 Model, RFCs, ADRs, Markdown specifications, Sequence Diagrams |
I combine architecture work with implementation and delivery:
🧱 Clear boundaries, maintainable designs and explicit architectural trade-offs
🧪 Automated testing across unit, integration and end-to-end levels
📝 Spec-driven delivery: short written specifications that turn requirements and technical constraints into clear decisions, a workable plan and checks that the delivered change meets the need
📝 RFCs, ADRs, code reviews and documentation that support team decisions
📊 Observability, rollout safety and production readiness
🤝 Mentoring and technical direction while staying close to the code
I integrate AI capabilities into real backend and enterprise workflows with the same engineering discipline applied to production systems:
- 🔗 LLM-enabled backend workflows built with Python and FastAPI
- 🔍 RAG-style retrieval for product and domain knowledge
- 🧠 LangGraph orchestration with explicit backend fallbacks
- 🛠️ MCP and tool orchestration for agentic systems
- 🔐 Security, observability, evaluation, reliable fallbacks and production readiness
Corbat COCO is an open-source CLI coding agent and reusable agent runtime for real-world automation.
- 🔄 Multi-step execution: inspect, plan, implement, test and refine
- ✅ Quality convergence with configurable validation thresholds
- 🧠 Multi-provider model support
- 🛡️ Configurable tools, permissions, sessions, events and workflows
- 🔌 MCP integration and extensible skills
Built for real repositories and reusable agent-powered products, not only isolated demos.
I work at the intersection of architecture and delivery, actively contributing to:
- 🧭 Technical direction and architecture decision-making
- 📝 RFCs, ADRs and system design
- 🔍 Code reviews, mentoring and engineering quality
- 🤝 Cross-functional delivery and team enablement
- 🚀 CI/CD, observability and safe production rollouts
- 🌐 Website: www.victormartingil.com
- 📄 CV: www.victormartingil.com/cv
- 📫 Email: victormartingil@gmail.com
- 💼 LinkedIn: linkedin.com/in/victor-martin-a1464397
Designing and delivering production-ready backend systems with pragmatic AI integration.