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For Companies At a Glance
- Tasks: Lead the development of innovative AI products and collaborate with cross-functional teams.
- Company: Join a forward-thinking organisation at the forefront of AI and data analytics.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Be a key player in revolutionising AI technology and making a real impact.
- Qualifications: PhD in a quantitative field and significant ML engineering experience required.
- Other info: Dynamic role with excellent career advancement opportunities in a collaborative environment.
The predicted salary is between 72000 - 108000 ÂŁ per year.
We are thrilled to introduce you to our team at the Chief Data and Analytics Office (CDAO) organization. As the driving force behind the firmwide adoption of artificial intelligence (AI) across our company, our dedicated team is responsible for overseeing data use, governance, and controls around the build, adoption and maintenance of cloud infrastructure, data and AI/ML products. With a focus on both effectiveness and responsibility, we strive to push the boundaries of innovation while ensuring ethical and sustainable practices. Join us on this exciting journey as we revolutionize the way we leverage data and analytics to shape the future of our organization.
As a Generative AI Executive Director within our CDAO organization, you will play a crucial role in ensuring the smooth operation and optimization of our LLM aided AI products. Our firm-wide team focuses on developing scalable LLM-based products and reusable back-end APIs. You will engage in close collaboration with cross-functional teams, including the ML Centre of Excellence, AI Research, Cloud Engineering, and others, to foster innovation and deliver solutions that yield a high Return-on-Investment (RoI). You will ensure that our APIs are built with scalability in mind, allowing them to efficiently handle a large number of requests without compromising performance. By designing APIs with a clear separation of concerns and well-defined interfaces, we enable other teams and developers to leverage our APIs to build their own ML products and solutions, fostering a culture of collaboration and efficiency.
Job Responsibilities
- Combine vast data assets with cutting-edge AI, including LLMs and Multimodal LLMs.
- Bridge scientific research and software engineering, requiring expertise in both domains.
- Collaborate closely with cloud and SRE teams while leading the design and delivery of production architectures.
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics.
- Significant experience in an individual contributor role in ML engineering.
- Proven track record in building and leading teams of experienced ML engineers/scientists.
- Solid understanding of the fundamentals of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms.
- Hands‑on experience in implementing distributed/multi‑threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.).
- Ability to understand and align with business expectations, and write clear and concise OKRs (Objectives and Key Results).
- Experience as a 'Responsible Owner' for ML services in enterprise environments.
- Excellent grasp of computer science fundamentals and SDLC best practices.
- Ability to understand business objectives and align ML problem definition.
- Strong communication skills to effectively convey technical information and ideas at all levels, building trust with stakeholders.
Preferred qualifications, capabilities, and skills
- Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray).
- Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints.
- Demonstrable experience in parameter‑efficient fine‑tuning, model quantization, and quantization‑aware fine‑tuning of LLM models.
- Hands‑on knowledge of Chain‑of‑Thoughts, Tree‑of‑Thoughts, Graph‑of‑Thoughts prompting strategies.
Generative AI - Executive Director in London employer: Jpmorgan Chase & Co.
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Generative AI - Executive Director in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and ML space. Attend meetups, webinars, or conferences where you can chat with industry leaders and potential colleagues. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs and AI. This is your chance to demonstrate your expertise and passion for the field. Share it on platforms like GitHub or even your own website to catch the eye of recruiters.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each role. Research the company’s values and projects, and align your application to show how you can contribute to their mission. This will make you stand out as a candidate who truly understands their needs.
✨Tip Number 4
Apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly helps us see your enthusiasm. Plus, it’s a great way to ensure your application gets into the right hands quickly. Let’s revolutionise data and analytics together!
We think you need these skills to ace Generative AI - Executive Director in London
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI and data analytics shine through. We want to see how excited you are about the potential of generative AI and how it can revolutionise our organisation.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to highlight your relevant experience in ML engineering and leadership. We love seeing how your background aligns with our mission, so don’t hold back on showcasing your achievements!
Be Clear and Concise: In your written application, clarity is key! Use straightforward language to explain your skills and experiences. We appreciate a well-structured application that gets straight to the point without unnecessary fluff.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your Stuff
Make sure you brush up on your knowledge of generative AI, LLMs, and the latest trends in machine learning. Be ready to discuss specific projects you've worked on, especially those that involved building scalable applications or APIs. This will show that you not only understand the theory but also have practical experience.
✨Showcase Collaboration Skills
Since this role involves working closely with cross-functional teams, be prepared to share examples of how you've successfully collaborated in the past. Highlight any experiences where you bridged gaps between technical and non-technical teams, as this will demonstrate your ability to communicate effectively and build trust.
✨Align with Business Goals
Understand the company's objectives and be ready to discuss how your work in AI can align with their business goals. Prepare to talk about how you've set and achieved OKRs in previous roles, as this will show that you can think strategically and contribute to the organisation's success.
✨Prepare for Technical Questions
Expect to face some tough technical questions related to ML engineering, statistics, and software development best practices. Brush up on your knowledge of frameworks like Ray and DeepSpeed, and be ready to explain complex concepts in a way that's easy to understand. This will help you stand out as a candidate who can communicate technical information clearly.
