At a Glance
- Tasks: Lead the development of AI tools that transform wealth management.
- Company: Orbis Group, a leader in innovative financial solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a dynamic team driving change in the wealth management industry.
- Why this job: Be at the forefront of AI innovation and make a real impact in finance.
- Qualifications: Experience with LLM/GenAI tools and strong collaboration skills.
The predicted salary is between 69750 - 85250 £ per year.
Orbis Group is seeking a Lead AI Engineer to sit at the centre of a transformative AI shift within a wealth management context.
You will work directly with business teams across the firm to identify high-value use cases and then design, build, and ship production AI tools that deliver real value.
The role requires experience shipping LLM/Gen AI tools to production, comfort collaborating with non-technical stakeholders, and preferring some exposure to wealth or asset management.
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Lead AI Engineer - Production GenAI for Wealth Management employer: Orbis Group
Orbis Group is an exceptional employer, offering a dynamic work environment where innovation meets security in the fast-paced trading sector. With a strong emphasis on employee growth and collaboration, you will have the opportunity to shape cutting-edge security strategies while working closely with senior leadership. Our culture fosters creativity and technical excellence, making it an ideal place for professionals eager to make a meaningful impact in cyber security.
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We think this is how you could land Lead AI Engineer - Production GenAI for Wealth Management
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We think you need these skills to ace Lead AI Engineer - Production GenAI for Wealth Management
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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