Senior Developers - Fintech - Java - Contract - URGENT
My client, a giant in the world of payments technology is looking to hire a Senior Java Developer to support the transformation and modernisation of their payments processing platform.
Please see the following:
What you will do:
Design, build, and ship high-quality microservices and event-driven capabilities that power modern experiences.
Partner with architecture, product, data science, and AI platform teams to embed machine learning and AI-assisted workflows in customer and operational journeys.
Lead the decomposition and modernisation of legacy components, applying domain-driven design, strangler patterns, and AI-accelerated refactoring techniques.
Curate, instrument, and document systems so AI copilots and autonomous runbooks can observe, learn, and act safely (telemetry, feature flags, guardrails).
Drive engineering excellence through thoughtful design reviews, automated testing, chaos/resilience practices, and codified reliability standards.
Improve developer experience with automated pipelines, infrastructure-as-code, policy-as-code, and AI-enabled tooling that shorten feedback loops.
Coach and mentor engineers on AI-first ways of working: prompt literacy, pair-programming with copilots, responsible-use guidelines, and experimentation discipline.
Balance transformation ambition with delivery commitments by making pragmatic, data-informed engineering trade-offs and communicating risks early.
What you will need to have:
Proven experience delivering software in complex, distributed, highly regulated environments.
Expert-level Java skills plus fluency with modern frameworks, testing libraries, and build tooling.
Hands-on experience with AWS (or comparable cloud) and building cloud-native, containerised services on Kubernetes.
Working knowledge of event-driven architectures and streaming platforms such as Kafka; able to model, test, and operate event flows at scale.
Experience with CI/CD, infrastructure automation, observability stacks, and Site Reliability/DevOps practices.
Demonstrated use of AI-assisted engineering tools (e.g., GitHub Copilot, internal copilots) or building ML/LLM-enabled services; comfort applying AI responsibly.
Strong collaboration and communication skills with the ability to influence cross-functional partners and navigate enterprise governance.