At a Glance
- Tasks: Design and deliver cutting-edge machine learning systems that drive real-world impact.
- Company: Join a global leader in financial services with a focus on innovation.
- Benefits: Competitive salary, career growth, mentorship, and flexible work options.
- Other info: Be part of a diverse team committed to inclusion and impactful change.
- Why this job: Shape the future of AI in finance while working with top-tier technologies.
- Qualifications: Experience in machine learning, AWS, Kubernetes, and a quantitative degree.
The predicted salary is between 62000 - 102000 £ per year.
Requirements
- Experience in machine learning engineering roles
- Degree in a quantitative discipline such as Computer Science, Mathematics, or Statistics
- Proven ability to develop and deploy business-critical, data-intensive applications
- Extensive experience with AWS and Kubernetes
- Proficiency with lower-level libraries such as Py Torch and Num Py
- Hands-on experience implementing distributed, multi-threaded, and scalable applications
- Experience with automated building, testing, and deployment pipelines
- Familiarity with higher-level interfaces such as Pydantic AI and Lang Graph
- Strong understanding of computer science fundamentals and development best practices
- Broad knowledge of MLOps tooling for versioning, reproducibility, and observability
- Ability to understand business objectives and align ML problem definitions
- Experience mentoring or leading teams
- Knowledge of agentic AI concepts
- Experience designing reusable libraries and services
- Interest in bridging scientific theory and enterprise-grade systems
- Passion for innovation and continuous learning
Responsibilities
- Design and deliver enterprise-grade machine learning systems
- Collaborate with cloud and SRE teams to build robust production architectures
- Translate scientific research into scalable ML solutions
- Develop and deploy business-critical, data-intensive applications
- Implement distributed, multi-threaded, and scalable applications
- Build, test, and deploy automated pipelines for ML solutions
- Leverage foundational libraries and services for re-use across teams
- Apply best practices in software engineering and computer science
- Utilize MLOps tools for versioning, reproducibility, and observability
- Align ML problem definitions with business objectives
- Mentor and support team members, with optional management responsibilities
Technologies
- Agentic AI
- AI
- AWS
- Cloud
- Support
- Kubernetes
- Machine Learning
- MLOps
- Py Torch
- numpy
- Security
More
We are a global leader in financial services and our Commercial & Investment Bank serves corporations, governments, and institutions in more than 100 countries.
We are building an AI platform and desktop application to automate document processing workflows at the worlds largest bank, with systems already operating at hundreds of documents per second and scaling rapidly.
You will join our Applied Artificial Intelligence and Machine Learning team within Commercial & Investment Banking, working at the intersection of software engineering and scientific research.
We offer career growth, mentorship, flexibility for individual contributors with optional management responsibilities, and the opportunity to help shape our team culture while driving impactful change.
We are committed to diversity, inclusion, and equal opportunity across our global workforce.
- last updated 36 week of 2026
- #J-18808-Ljbffr
Machine Learning Engineering & Applied AI ML Lead in London employer: JP Morgan Chase
Morgan is an exceptional employer, offering a dynamic work culture that prioritises diversity and inclusion while fostering employee growth through comprehensive coaching and development opportunities. As a global leader in financial services, we empower our teams to drive impactful product management and AI enablement, ensuring that every employee can contribute meaningfully to our clients' success in a collaborative environment located at the heart of the financial sector.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineering & Applied AI ML Lead in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at JP Morgan Chase or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to JP Morgan Chase.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like JP Morgan Chase.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like JP Morgan Chase that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Machine Learning Engineering & Applied AI ML Lead in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at JP Morgan Chase.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at JP Morgan Chase and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at JP Morgan Chase
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If JP Morgan Chase uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
