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- Tasks: Lead the development of generative AI solutions and mentor a talented team.
- Company: Join J.P. Morgan, a leader in financial services with a focus on innovation.
- Benefits: Competitive salary, career growth, and opportunities to work with cutting-edge technology.
- Other info: Collaborate with cross-functional teams in a dynamic and innovative environment.
- Why this job: Shape the future of Asset and Wealth Management Risk with your expertise.
- Qualifications: Master's or Bachelor's in Computer Science, experience with Python and AWS.
The predicted salary is between 80000 - 120000 £ per year.
J.P. MORGAN in Glasgow is seeking a Lead Agentic Gen AI / Natural Language Querying Engineer at the Vice President level. This role is crucial in driving generative AI solutions for Risk Technology. You will mentor a talented team, shape technology strategies, and collaborate with cross-functional partners.
The ideal candidate should have a Master's or Bachelor's degree in Computer Science or related field, experience with Python, AWS, and a strong understanding of AI frameworks.
Join us to innovate and shape the future of Asset and Wealth Management Risk.
Lead Gen AI & NLP Architect — Multi-Agent Systems employer: J.P. Morgan
Contact Detail:
J.P. Morgan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Gen AI & NLP Architect — Multi-Agent Systems
✨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 GitHub repo showcasing your projects, especially those related to AI and NLP. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to generative AI and risk technology. We can help you with mock interviews to boost your confidence!
✨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 take that extra step.
We think you need these skills to ace Lead Gen AI & NLP Architect — Multi-Agent Systems
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead Gen AI & NLP Architect role. Highlight your experience with Python, AWS, and any relevant AI frameworks. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about generative AI and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Projects: If you've worked on any projects related to AI or multi-agent systems, make sure to mention them. We’re keen to see real-world applications of your skills, so don’t hold back on sharing your achievements!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you get all the updates directly from us. Let’s make this happen together!
How to prepare for a job interview at J.P. Morgan
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python, AWS, and the AI frameworks relevant to the role. Brush up on your technical skills and be ready to discuss specific projects where you've applied these technologies.
✨Showcase Your Leadership Skills
As a Lead Gen AI & NLP Architect, you'll be mentoring a team. Prepare examples of how you've successfully led teams or projects in the past, focusing on your approach to collaboration and problem-solving.
✨Understand the Business Context
Familiarise yourself with J.P. Morgan's Asset and Wealth Management Risk strategies. Being able to connect your technical expertise to their business goals will show that you’re not just a techie but also a strategic thinker.
✨Prepare for Behavioural Questions
Expect questions about teamwork, conflict resolution, and decision-making. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your experiences effectively.