Requirements Strong experience in LLM/AI engineering, including hands-on work with large language models
Proven experience building LLM-based applications, including RAG pipelines, agents, embeddings, and vector databases
Solid experience leading engineering teams, ideally within AI/ML or data platforms
Strong experience with agile software development methodologies and leading agile teams
Hands-on experience with cloud platforms (Azure preferred) and modern data/AI ecosystems
Experience with Databricks, Snowflake, or similar data platforms
Familiarity with AI agents, orchestration frameworks (e.g., LangChain, Semantic Kernel, AutoGen, etc.)
Strong experience designing and delivering API-driven, distributed, cloud-native systems
Experience with MLOps/LLMOps, model lifecycle management, and production deployment of AI systems
Strong programming skills in Python, and familiarity with C++, C#, or similar languages
Experience working with large-scale data systems and pipelines
Domain knowledge in financial markets (Fixed Income, Multi-Asset analytics) is a strong plus
Strong understanding of software architecture, scalability, and system design
Excellent communication and stakeholder management skills
Degree (Master's or equivalent) in Computer Science, Engineering, Mathematics, or related field
~10+ years of experience in technology, AI, or financial services
What the job involves The Tech Lead, Quantitative Analytics Engineering (AI/LLM Focus) will lead a high-performing team of quantitative analytics and AI engineers, driving the design, development, and delivery of next-generation Analytics and AI-powered solutions
This role will combine deep expertise in Large Language Models (LLMs), AI/ML engineering, and cloud-native platforms with strong leadership experience to build scalable, intelligent analytics products for LSEG's global client base
The successful candidate will be responsible for leading the development and delivery of advanced Analytics and AI solutions, with a particular focus on LLM-powered applications, agent-based systems, and intelligent data platforms
This role requires hands‐on technical leadership across AI/LLM engineering, agile delivery, and cloud-native architectures, while managing a team of engineers and external partners. You will collaborate closely with Analytics Business, Product, and Research teams to deliver innovative solutions aligned with LSEG's strategic partnership with Microsoft and its cloud-first vision
You will play a key role in building and scaling LLM-driven capabilities, including AI-assisted analytics, automation, and decision-support tools for financial markets
Lead the design, development, and deployment of LLM-powered analytics solutions, including agent-based systems, copilots, and AI-driven workflows
Drive adoption of AI/LLM technologies across Analytics products, including prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and model evaluation
Lead and manage a team of engineers, fostering a strong engineering culture focused on innovation, delivery, and continuous learning
Own the LLM engineering roadmap, aligning with business priorities and Microsoft partnership initiatives
Deliver high-quality Analytics and AI product solutions to clients in collaboration with Product, Research, and Sales teams
Architect and implement cloud-native solutions leveraging platforms such as Azure, Databricks, and Snowflake
Design and deliver scalable data pipelines and AI workflows integrating structured and unstructured financial data
Lead development of API-first, SaaS/PaaS-based AI products, including analytics, risk models, and intelligent reporting tools
Extend and support a multi-cloud and hybrid cloud platform for AI and Analytics workloads
Drive agile engineering practices, including CI/CD, MLOps/LLMOps, and DevOps for AI systems
Collaborate with cross-functional teams to integrate AI agents and automation tools into business workflows
Ensure best practices in model governance, risk, explainability, and compliance, especially in financial services
Mentor and develop engineers in AI/ML, LLM technologies, and modern software engineering practices
Foster a culture of innovation, experimentation, and continuous improvement
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