PROFESSIONAL SUMMARY

Backend Software Engineer with 10+ years of production experience building and operating business-critical Java services (Java 8?23, including Java 17+) at high scale within large, established codebases. Strong track record designing and consuming REST APIs, building microservices and event-driven distributed systems with Apache Kafka and RabbitMQ, and owning technical solutions independently from problem framing through production rollout. Practises strong software engineering fundamentals ? TDD, code review, CI/CD ? and has set these standards across teams, not just followed them. Comfortable with Spring Boot, AWS, and Kubernetes in production, and experienced translating infrastructure and reliability work into measurable outcomes ($24M and 70% cost reductions). Clear, professional English communicator with hands-on interest in applying AI/ML to engineering workflows, including building production LLM-integrated systems.

CORE SKILLS

• Java Backend Development: Java 8?23 (10+ years, including Java 17+), Kotlin, Spring Boot 3.x, Spring MVC, Spring Data (JPA/Hibernate/JDBC); building and operating production services within large, established codebases
• Microservices & Distributed Systems: Service decomposition, event-driven architecture, CQRS, Domain-Driven Design (DDD), high-concurrency systems, API Gateway patterns
• Messaging & Streaming: Apache Kafka (event fan-out, high-throughput messaging), RabbitMQ, AWS SQS/SNS, ActiveMQ
• REST & API Design: Designing and consuming REST APIs (OpenAPI/Swagger, contract-first, versioning, API lifecycle management), GraphQL, gRPC, SOAP
• Engineering Best Practices: TDD, JUnit 5, Mockito, Testcontainers, SonarQube quality gates, code review standards, CI/CD (GitLab CI, Jenkins)
• Cloud & Infrastructure: AWS (EKS, S3, RDS, Lambda, SQS, SNS, DynamoDB), Kubernetes, Docker, Terraform (IaC), GCP familiarity
• Observability: Grafana, Prometheus, NewRelic, Datadog for production logging, metrics, and distributed monitoring
• Independent Ownership: Track record of owning technical solutions end-to-end, from business problem to production, with minimal oversight, across distributed international teams
• AI/ML Interest & Applied Experience: Hands-on use of AI coding assistants (Cursor, Claude Code, GitHub Copilot); built production NL-to-SQL and Sovereign-RAG systems with LLM integration, LangChain, and agentic workflows ? active interest in applying AI/ML to engineering and security-relevant problem spaces

KEY ACHIEVEMENTS

• $24M Annual Savings: Scoped, designed, and built a custom high-concurrency API Gateway (Java, Spring Boot, REST) from scratch within a large existing codebase, replacing a costly third-party dependency end-to-end; recognised by the VP of Engineering
• 70% Server Cost Reduction: Led decomposition of a large Java monolith into independently deployable microservices using CQRS, DDD, and event-driven architecture (Apache Kafka, Docker, Kubernetes), delivered incrementally with zero disruption to live systems
• 40% Cloud Cost Reduction: Drove a Kubernetes right-sizing initiative on AWS EKS, translating infrastructure decisions into measurable business outcomes
• Near-Instant Deployments: Designed an automated migration engine for a global airline cargo platform, cutting deployment time from 7 days to near-instant through clean API design and workflow orchestration

AI RESEARCH & INNOVATION (2025?2026)

• Built a production NL-to-SQL pipeline (Java 23, LLM integration, DDD-structured domain layer) with clean architecture, TDD, and JDBC-backed relational persistence, from problem scoping to deployment and maintenance
• Built a Sovereign-RAG system with agentic workflows (LangChain, LLM-as-a-Judge validation, Neo4j vector DB), event-driven design, secure-by-architecture, production-ready