Senior Software Engineer - Java, Cloud and AI

Senior Software Engineer - Java, Cloud and AI
London, UK (5 days Onsite)

As a Senior Software Engineer in Enterprise Platforms, you will be part of an agile team building and evolving platform services, integrations, and data-enabled solutions that support enterprise communication data and regulatory controls. You will design, develop, and operate secure, reliable, and scalable systems that power business controls, analytics, reporting, and AI/ML use cases. You will partner closely with domain teams, platform engineering, and operations to deliver production-grade services and curated datasets aligned to platform standards, resiliency expectations, and architecture principles. You will play a key role in raising engineering quality, accelerating delivery through automation, and driving continuous improvement across the platform.

Job Responsibilities:

Executes software solutions including design, development, and technical troubleshooting, with the ability to think beyond routine or conventional approaches to build durable solutions and break down complex technical problems using Java, Spring, and Spring Boot
Creates secure and high-quality production code for microservices and REST APIs, and maintains synchronous and asynchronous processing components that integrate reliably with dependent systems
Produces architecture and design artifacts for complex applications (e.g., service boundaries, data flows, interface contracts, resiliency patterns) and is accountable for ensuring design constraints are implemented and validated through software development
Gathers, analyses, synthesizes, and develops visualizations and reporting from large, diverse datasets to drive continuous improvement in application performance, resiliency, and operational stability
Proactively identifies hidden issues and patterns in code, logs, and data, using these insights to improve coding hygiene, reduce defects, and strengthen system architecture and scalability
Contributes to software engineering communities of practice and technical forums that explore new and emerging technologies, patterns, and engineering standards
Adds to a team culture of diversity, opportunity, inclusion, and respect through collaboration, mentorship, and constructive feedback
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation across build, test, release, and operations.

Required Qualifications, Capabilities, and Skills:

Hands-on practical experience in system design, application development, testing, and operational stability for production systems
Demonstrable ability to code in Java with Spring and Spring Boot, including microservices architecture and REST API development
Experience developing, debugging, and maintaining enterprise-scale applications in a large corporate environment using one or more modern programming languages and database querying languages (MS SQL Server, Oracle, SQL)
Overall knowledge of the Software Development Life Cycle, including requirements, design, development, testing, release, and support
Solid understanding of agile delivery practices, including CI/CD, application resiliency, and security fundamentals
Demonstrated knowledge of software applications and technical processes within a technical discipline such as cloud (e.g., deploying and operating services in cloud or hybrid environments)
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Preferred Qualifications, Capabilities, and Skills:

Experience building data products on Databricks using lakehouse patterns, including Delta Lake and medallion architecture
Familiarity with enterprise data platforms and data mesh principles, including domain-aligned datasets and well-defined data contracts
Exposure to data quality, observability, and metadata management practices and tools (data validation frameworks, lineage, monitoring, and alerting)
Experience enabling analytics, reporting, and AI/ML workloads through curated datasets, performance-optimized pipelines, and reliable service interfaces
Experience developing or supporting regulatory controls use cases, including auditability, traceability, and evidence-driven control outcomes
Experience with Infrastructure as Code using Terraform and/or CloudFormation, including environment provisioning and repeatable deployments
Experience designing event-driven architectures using AWS services such as SQS, SNS, and Event Bridge, including asynchronous processing and resiliency patterns.