Development Engineer - Mid Ops Java + AI

Responsibilities:

Design and build scalable, reliable backend systems that power core enterprise platforms.

Develop and maintain microservices and event-driven architectures, ensuring systems are loosely coupled and highly available.

Take ownership of end-to-end system development, from design to deployment and production support.

Ensure systems are production-ready by implementing strong observability, monitoring, and alerting mechanisms.

Optimise system performance by improving latency, throughput, and resource utilisation.

Collaborate with cross-functional teams to define APIs, service contracts, and data models.

Implement resilience patterns such as retries, circuit breakers, and graceful degradation.

Contribute to and improve CI/CD pipelines and deployment processes for faster and safer releases.

Write clean, maintainable, and well-tested code, following best engineering practices.

Use production data and metrics to continuously improve system reliability and performance.

Participate in code reviews, technical discussions, and architectural decisions.

Support and mentor team members, contributing to overall engineering excellence.

Explore opportunities to integrate automation or AI-driven enhancements where applicable.

Requirements:

Strong expertise in Java (Spring Boot) and working knowledge of Kotlin for building scalable backend services.

Deep understanding of distributed systems design, including scalability, fault tolerance, and eventual consistency.

Hands-on experience with microservices architecture and event-driven systems using messaging platforms like Kafka.

Proficiency in working with both relational databases (PostgreSQL, Oracle) and NoSQL databases (MongoDB, Cassandra, Redis), including data modelling and performance optimisation.

Experience with API design, service contracts, and building high-throughput, low-latency services.

Familiarity with cloud-native development in environments like AWS, GCP, or Azure.

Strong experience in containerization (Docker) and orchestration tools like Kubernetes.

Knowledge of CI/CD pipelines, automated testing, and deployment strategies (blue-green, canary releases).

Understanding of observability practices, including logging, monitoring, and distributed tracing.

Exposure to search technologies like ElasticSearch.

Solid foundation in data structures, algorithms, and problem-solving.

Working knowledge of secure coding practices and authentication/authorisation mechanisms.

Exposure to AI-assisted development tools and a basic understanding of integrating AI-driven components (good to have).

Leverage AI coding assistants for development and utilise them to generate better quality output.

Support integration of AI or automation-driven components into the backend systems.

Ensure AI-enabled components operate within defined reliability and safety boundaries.

AI exposure is valued, but distributed systems engineering remains the primary focus of this role.