VP Lead Agentic Gen AI & NLQ Engineer

VP Lead Agentic Gen AI & NLQ Engineer

Full-Time 60000 - 80000 £ / year (est.) No working from home possible

At a Glance

  • Tasks: Lead a team to innovate AI solutions and collaborate across functions.
  • Company: Join a forward-thinking company in the heart of Glasgow.
  • Benefits: Competitive salary, leadership opportunities, and a chance to shape the future.
  • Other info: Exciting role with significant career growth potential.
  • Why this job: Make a real impact in Asset and Wealth Management Risk with cutting-edge technology.
  • Qualifications: Strong background in data science, AI engineering, and Python required.

The predicted salary is between 60000 - 80000 £ per year.

is seeking a Lead Agentic Gen AI / Natural Language Querying Engineer – Vice President in Glasgow, Scotland. In this role, you will lead a team to drive innovation in AI solutions for Risk Technology and collaborate with cross-functional partners. The ideal candidate will have a strong background in data science, AI engineering, and Python, with experience in deploying solutions on AWS. This position offers a significant opportunity to shape the future of Asset and Wealth Management Risk.

VP Lead Agentic Gen AI & NLQ Engineer employer: 慨正橡扯

At 慨正橡扯, we pride ourselves on fostering a dynamic and inclusive work culture that encourages innovation and collaboration. As a VP Lead Agentic Gen AI & NLQ Engineer in Glasgow, you will not only lead cutting-edge projects in AI but also benefit from extensive professional development opportunities and a supportive environment that values your contributions. Join us to be part of a forward-thinking team that is shaping the future of Asset and Wealth Management Risk while enjoying the vibrant culture and lifestyle that Glasgow has to offer.

Contact Details:

慨正橡扯 Recruitment Team

We think you need these skills to ace VP Lead Agentic Gen AI & NLQ Engineer

AI Engineering
Data Science
Python
AWS Deployment
Team Leadership
Innovation in AI Solutions
Collaboration with Cross-Functional Partners