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- Tasks: Lead the development of AI/ML solutions and mentor fellow engineers.
- Company: Top financial services firm with a focus on innovation.
- Benefits: Attractive salary, flexible working options, and career advancement opportunities.
- Why this job: Join a dynamic team to shape the future of technology in finance.
- Qualifications: Strong ML background, Python skills, and AWS experience required.
- Other info: Collaborative environment with a chance to make a real impact.
The predicted salary is between 43200 - 72000 £ per year.
A leading financial services firm is seeking a Lead Software Engineer within Client Onboarding Technology. You will spearhead multi-agent systems and advanced ML solutions, responsible for hands-on development and mentorship of engineers.
Ideal candidates have a strong background in Machine Learning Engineering, proficiency in Python, and experience with AWS. This role offers an opportunity to innovate and drive value across teams, engaging with technical and business stakeholders in Glasgow.
Lead AI/ML Engineer – Agentic & Multi-Agent Systems in Glasgow employer: Jpmorgan Chase & Co.
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI/ML Engineer – Agentic & Multi-Agent Systems in Glasgow
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at the company you're eyeing. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio or a GitHub repo showcasing your projects in AI/ML. This is your chance to demonstrate your hands-on experience and creativity in solving real-world problems.
✨Tip Number 3
Ace the interview by practising common technical questions and scenarios related to multi-agent systems and ML solutions. 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 noticed. Plus, we love seeing candidates who take that extra step to engage with us directly.
We think you need these skills to ace Lead AI/ML Engineer – Agentic & Multi-Agent Systems in Glasgow
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Machine Learning Engineering and Python. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI/ML and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Leadership Skills: Since this role involves mentorship, highlight any previous experience you have in leading teams or projects. We’re looking for someone who can inspire others and drive innovation, so let us know how you’ve done that before!
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 a few clicks and you’re done!
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your Tech Inside Out
Make sure you brush up on your Machine Learning and Python skills. Be ready to discuss specific projects you've worked on, especially those involving multi-agent systems. The more you can demonstrate your technical expertise, the better!
✨Showcase Your Leadership Skills
Since this role involves mentorship, think of examples where you've led a team or guided junior engineers. Prepare to share how you approach problem-solving and foster collaboration within a team.
✨Understand the Business Context
Research the financial services firm and understand their client onboarding process. Be prepared to discuss how your technical solutions can drive value for the business and improve client experiences.
✨Engage with Stakeholders
This role requires interaction with both technical and business stakeholders. Practice explaining complex technical concepts in simple terms. Show that you can bridge the gap between tech and business needs effectively.
