At a Glance
- Tasks: Lead a team to design and develop AI-powered analytics solutions.
- Company: Join LSEG and Microsoft in a strategic partnership for innovative tech.
- Benefits: Enjoy career growth, flexible work, healthcare, and wellbeing initiatives.
- Other info: Mentorship opportunities and a culture of innovation await you.
- Why this job: Make an impact with cutting-edge AI technologies in a dynamic environment.
- Qualifications: Strong experience in AI engineering and leading agile teams required.
The predicted salary is between 80000 - 100000 £ per year.
LSEG and Microsoft have entered a strategic partnership for next-generation data, analytics and cloud infrastructure solutions. This role, the Tech Lead, Quantitative Analytics Engineering (AI/LLM Focus), leads a high-performing team of quantitative analytics and AI engineers, driving design, development and delivery of AI-powered solutions across LSEG’s global client base.
Responsibilities
- Lead 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 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 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.
Qualifications
- 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 such as 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.
Benefits
- Career growth leading a commercially focused tech team in a high‑profile business.
- Client‑facing financial applications design and development opportunity.
- Leadership opportunity to determine strategic technology direction for analytics products.
- Cutting‑edge development on Microsoft Office & Azure platforms and cloud technologies.
- Hybrid blended workplace approach; flexible work arrangements.
- Healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
We are proud to be an equal opportunities employer. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. We can reasonably accommodate applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.
AI Analytics Engineering Lead (LLM) – Hybrid employer: LSEG
LSEG offers an exceptional work environment for the AI Analytics Engineering Lead, fostering a culture of innovation and continuous learning within a hybrid workplace. Employees benefit from career growth opportunities, cutting-edge technology projects, and a strong focus on wellbeing, including healthcare and flexible work arrangements. With a commitment to diversity and inclusion, LSEG is dedicated to creating a supportive atmosphere where every team member can thrive.
StudySmarter Expert Advice🤫
We think this is how you could land AI Analytics Engineering Lead (LLM) – Hybrid
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or a GitHub repo showcasing your projects, especially those related to AI and LLMs. It’s a great way to demonstrate what you can do beyond the application.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to AI analytics. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace AI Analytics Engineering Lead (LLM) – Hybrid
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with LLMs, AI engineering, and any relevant projects you've led. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and analytics, and how your background aligns with our mission at StudySmarter. Keep it engaging and personal – we love a good story!
Showcase Your Projects:If you've worked on any LLM-based applications or AI projects, make sure to mention them! Include links or descriptions of your work to give us a taste of your capabilities. We’re keen to see your hands-on experience!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just follow the prompts!
How to prepare for a job interview at LSEG
✨Know Your LLM Inside Out
Make sure you’re well-versed in large language models and their applications. Brush up on your experience with RAG pipelines, embeddings, and vector databases, as these will likely come up during the interview.
✨Showcase Your Leadership Skills
Prepare examples of how you've led engineering teams, especially in AI/ML contexts. Highlight your experience with agile methodologies and how you've fostered a culture of innovation and continuous learning within your teams.
✨Demonstrate Cloud Expertise
Since this role involves cloud-native solutions, be ready to discuss your hands-on experience with Azure, Databricks, and Snowflake. Share specific projects where you’ve designed scalable data pipelines or implemented MLOps practices.
✨Communicate Effectively
Strong communication skills are key for this role. Practice articulating complex technical concepts clearly and concisely, especially when discussing collaboration with cross-functional teams and stakeholder management.
