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Java software engineer

Slough
Insight International (UK) Ltd
Java software engineer
Posted: 23 May
Offer description

Role : Java Developer

Location : London, UK

Contract role


Java engineer with 6+ years of experience. Essential skills include Spring boot, Java 17+, Kafka. --- gbriton


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, internalcopilots) 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

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