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LSEG

Global financial market infrastructure and data

Java/Spring AI Technical Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBucharest, RomaniaNo salary listedIn-person

About the Job

Requirements

  • Significant experience as a software developer.
  • Expert Java and Spring Boot skills, with strong experience building modular, testable enterprise services.
  • Experience applying Spring Modulith, jMolecule, and ArchUnit to enforce clear domain boundaries and architectural rules.
  • Strong fundamentals in APIs, distributed systems, modular architecture, data design, scalability, resilience, and observability.
  • Expertise in relational database engines and managed RDS platforms, including PostgreSQL and cloud-native variants.
  • Strong understanding of storage systems and fit-for-purpose data platforms, including transactional stores, document databases, search engines, vector stores, object storage, and event or streaming data layers.
  • Expertise in schema design, query tuning, indexing, partitioning, replication, high availability, disaster recovery, data residency, transaction consistency, and backup and restore for business-critical systems.
  • Hands-on experience with vector databases and retrieval-augmented generation (RAG) patterns, including embeddings, chunking, indexing, retrieval, and grounding AI outputs in trusted enterprise data.

Responsibilities

  • Design modern, modular, AI-optimised software systems and guide engineering teams across the full solution lifecycle.
  • Combine hands-on engineering with architectural vision to ensure solutions are scalable, resilient, observable, large language model-compatible, and aligned with modern software development lifecycle and AI-era development standards.
  • Drive decisions around system decomposition, modularity, interoperability, and domain boundaries, introducing patterns such as Spring Modulith, jMolecule domain-driven design abstractions, event architecture, and AI-assisted software development lifecycle workflows, including agents, model context protocol servers, and spec-driven development.
  • Optimise project structures, documentation, automation, and governance frameworks for modern large-language-model-driven development teams.
  • Deliver high-quality, maintainable, future-proof enterprise systems and accelerate delivery through AI-native engineering practices.
  • Provide a framework for software or system development that results in high-quality IT solutions.
  • Build a strong understanding of Capital Markets business processes and use that context to shape architecture decisions.
  • Mentor and upskill engineers, making complex topics clear and practical for junior developers.
  • Operate with high autonomy and entrepreneurial drive, spotting opportunities and leading improvement initiatives.
  • Champion adoption of modern technologies, engineering standards, and AI-enabled practices across the broader Capital Markets group.
  • Challenge the status quo constructively while balancing innovation with security, resilience, auditability, and risk control.
  • Develop others and remove barriers to cross-team and global collaboration.
  • Share expertise through architecture forums, workshops, brown-bag sessions, and other internal knowledge-sharing channels.
  • Promote clean code, automation, and strong software development lifecycle discipline across development, testing, and delivery.
  • Model domains with domain-driven design, events, and asynchronous patterns; assess command query responsibility segregation or event sourcing where they add value.
  • Design cloud-native systems and data platforms using technologies such as Postgres, MongoDB, Elasticsearch, and AWS-led cloud services.
  • Use spec-driven development to turn requirements into effective prompts, code, tests, and documentation.
  • Structure repositories, modules, and design artefacts so they are readable and usable by large-language-model-based tools.
  • Define practical prompting, review, and governance patterns that improve output quality and traceability.
  • Apply AI agents to code generation, refactoring, dependency mapping, documentation, and design review.
  • Integrate model context protocol-compatible tooling into engineering workflows and continuous integration/continuous delivery pipelines.
  • Design deterministic, well-scoped, side-effect-safe services and tools that can be safely orchestrated by agents.

About the Company

LSEG

Global financial market infrastructure and data

LSEG provides global financial market infrastructure and data across the full value chain. It delivers data, analytics, and risk-management tools, benchmarks via FTSE Russell, regulatory and financial crime risk services, and trading, clearing, and settlement through its Capital Markets and Post Trade divisions, typically accessed via subscriptions, licensing, and fees. The company differentiates itself by offering an integrated, end-to-end platform that covers pre-trade data and analytics to post-trade processing with a broad, global footprint. Its goal is to grow over the long term by leveraging its diversified portfolio and international reach to create value for shareholders and customers.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1801

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