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LSEG

Global financial market infrastructure and data

Senior AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeTaguig, Metro Manila, PhilippinesNo salary listedIn-person

About the Job

Requirements

  • At least 8 years of professional software engineering experience.
  • At least 1 year of exposure to AI or large language model solution development.
  • Proven delivery of complex, high-impact systems.
  • Extensive experience in system and application architecture.
  • Expertise in Java and Python, with exposure to additional modern programming languages.
  • Experience with distributed systems, microservices, API design, and event-driven patterns.
  • Experience with prompt engineering, evaluation, fine-tuning, embeddings, retrieval-augmented generation, and model deployment.
  • Experience with vector stores, caching, streaming, and persistence strategies.
  • Experience with prompt management tools such as Langfuse and experimentation frameworks.
  • Experience designing secure, scalable workloads on AWS, Azure, or GCP.
  • Experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code.
  • Knowledge of identity, secrets management, and secure software development lifecycle practices.
  • Experience with metrics, tracing, log aggregation, and service-level objective/service-level agreement design.
  • Advanced analytical and problem-solving ability.
  • Ability to take technical ownership and make pragmatic decisions.
  • Ability to communicate technical information clearly and concisely.
  • Ability to operate independently while enabling team success.

Responsibilities

  • Design and implement complex services and platforms.
  • Write clean, maintainable, well-tested code in Java, Python, and other high-level languages.
  • Tune performance and perform root-cause analysis of critical issues.
  • Design and refine prompt engineering strategies for production large language model applications.
  • Integrate, optimize, and evaluate large language models using retrieval-augmented generation, agentic AI, embeddings, vector databases, and prompt management tools.
  • Lead model selection, fine-tuning, and deployment strategies for financial use cases.
  • Build and maintain AI evaluation frameworks and automated testing pipelines.
  • Champion observability, AI operations, and real-time monitoring across distributed systems.
  • Drive resilience patterns, including circuit breakers, retries, and graceful degradation, to support 99.99% uptime.
  • Design cost-effective AI infrastructure optimized for latency and throughput.
  • Partner with Product, QA, Architecture, Data Science, and Compliance teams.
  • Conduct technical design reviews and elevate engineering standards across the organization.
  • Mentor senior engineers and lead architectural decisions.
  • Drive technical strategy for AI adoption across multiple product lines.
  • Track emerging AI and machine learning frameworks, techniques, and tooling.
  • Prototype and assess new approaches for production adoption.
  • Build proofs of concept demonstrating business value and technical feasibility.
  • Enforce automated testing, CI/CD, and secure coding.
  • Advocate for infrastructure-as-code and continuous improvement practices.

Desired Qualifications

  • Hands-on experience building AI infrastructure, including evaluation pipelines, prompt/version management, vector/embedding stores, and model serving, and optimizing or fine-tuning models for latency, quality, and cost.
  • Experience with model evaluation harnesses and automated prompt regression.
  • Background in cost optimization for AI workloads.
  • Contributions to open-source projects or technical publications.

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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