Senior AI Engineer

About the role

LSEG is hiring a Senior AI Engineer in Taguig City. The role drives end-to-end design, development and delivery of scalable, secure, high-performance software and AI solutions, combining deep engineering expertise with hands-on Large Language Model (LLM) integration, prompt engineering and engineering leadership.

Key responsibilities

• Design and implement complex services and platforms, writing clean, well-tested code in Java, Python and other high-level languages, and tune performance and root-cause analyse critical issues.
• Design and refine prompt engineering strategies for production LLM applications, integrate and evaluate LLMs with RAG, Agentic AI, embeddings, vector databases and prompt management tools, and lead model selection, fine-tuning and deployment for financial use cases.
• Build and maintain AI evaluation frameworks and automated testing pipelines, and champion observability, AI Ops and real-time monitoring.
• Drive resilience patterns such as circuit breakers, retries and graceful degradation for 99.99% uptime, and design cost-effective AI infrastructure optimised for latency and throughput.
• Partner with Product, QA, Architecture, Data Science and Compliance teams, conduct technical design reviews, mentor senior engineers and lead architectural decisions.
• Track emerging AI/ML frameworks, prototype new approaches, build proof-of-concepts and enforce automated testing, CI/CD, secure coding and infrastructure-as-code.

Required profile

• 8+ years of professional software engineering.
• 1+ year of exposure to AI / LLM solution development.
• Proven delivery of complex, high-impact systems and extensive experience in system and application architecture.
• Nice to have: hands-on AI infrastructure experience, model evaluation harnesses, cost optimisation background, open-source contributions or technical publications.

Required skills

• Expert Java and Python, distributed systems, microservices, API design and event-driven patterns.
• Prompt engineering, evaluation, fine-tuning, embeddings, RAG and model deployment.
• Vector stores, caching, streaming, persistence strategies.
• Cloud (AWS, Azure or GCP), CI/CD, Docker, Kubernetes, infrastructure-as-code.
• Identity, secrets management, observability (metrics, tracing, logs) and SLO/SLA design.