At a Glance
- Tasks: Design and build AI solutions that create real business value in a dynamic environment.
- Company: Join JPMorgan Chase, a leading global financial services firm with a focus on innovation.
- Benefits: Competitive salary, diverse culture, and opportunities for professional growth.
- Other info: Opportunity for international travel and hands-on experience with cutting-edge AI projects.
- Why this job: Be at the forefront of AI technology and make a tangible impact in the financial sector.
- Qualifications: Experience in AI/ML engineering, advanced Python skills, and a collaborative mindset.
The predicted salary is between 72000 - 88000 £ per year.
We're looking for a hands-on, business-facing AI engineer who thrives at the intersection of technology and the front line. Join the ranks of top talent at one of the world's most influential companies. As an AI Solutions Engineer (Forward Deployed) - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you will be deployed directly into business contexts to identify where AI can create value, then design, build, and ship the solutions that realise it. You will operate at the front of the team's engagement model: turning ambiguous, surfacing opportunities into scoped, working agentic AI and machine learning solutions, and partnering with the core AIML team to productionise and scale what proves out. This is a Vice President-level role and an integral part of the IPB Tech AIML team, reporting to the Head of AI, IPB Tech.
Job responsibilities:
- Embeds with IPB advisors, business teams, and product partners (e.g. across Investment, Client experience, and surfacing IPB-first use cases) to discover and frame high-value AI/ML opportunities.
- Rapidly prototypes and builds agentic AI and LLM-powered solutions full-stack and end-to-end (backend, data, and lightweight interfaces as needed) to demonstrate value quickly, then hardens and scales them with the core AIML team.
- Owns solutions end-to-end during the engagement: problem framing, build, demo, iteration, and hand-off to production.
- Acts as the primary technical translator between business/product stakeholders and the engineering team, shaping opportunities into funded, well-scoped workstreams.
- Balances speed of iteration with the team's engineering, Responsible AI, and control standards (guardrails, evaluation, observability).
- Feeds reusable patterns, skills, and learnings back into the team's platform so each engagement compounds.
- Contributes to the team's GenAI education and knowledge-sharing, and mentors junior engineers on solutioning and delivery.
- Champions the firm's culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills:
- Formal training or certification on AI/ML engineering concepts and applied experience.
- Advanced Python and full-stack/generalist engineering ability - backend, data, and lightweight front-end - to ship a working end-to-end solution, with modern software engineering practices (testing, code review, version control).
- Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot) as a core part of day-to-day development, with the judgement to know when to lean on them and when not to.
- Practical experience with Large Language Models, including prompt engineering, RAG, and/or agentic frameworks.
- Hands-on experience taking solutions from prototype to production, including CI/CD, containerisation, and cloud-native deployment.
- Proven ability to work directly with non-technical stakeholders - eliciting needs, framing problems, demoing, influencing, and building trust across technical and business audiences.
- Comfort with ambiguity and the ability to switch context quickly across multiple problem domains, stakeholders, and engagements; a bias to ship and learn while maintaining engineering quality.
- Product mindset: prioritises by user value and outcomes, with the judgement to decide what to build, what to cut, and what 'good enough to prove value' looks like.
- Commercial acumen: spots where AI creates measurable business value, frames success metrics, and builds the case to fund and scale what works.
- Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field (or equivalent applied experience).
Preferred qualifications, capabilities, and skills:
- Industry-recognised cloud/GenAI certification (e.g., AWS Certified Generative AI Developer - Professional, or similar).
- Prior forward-deployed, solutions-engineering, consulting, or client-facing engineering experience.
- Openness to periodic on-site embedding with business teams and occasional international travel, as engagements benefit from it.
- Experience within financial services, particularly wealth, private banking, or asset management.
- Experience designing or contributing to AI governance, model validation, or guardrail frameworks.
- Familiarity with JPM-internal AI/ML infrastructure and governance for internal candidates.
AI Lead Solutions Engineer -Forward Deployed in Scotland 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 AI Lead Solutions Engineer -Forward Deployed in Scotland
✨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
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✨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
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We think you need these skills to ace AI Lead Solutions Engineer -Forward Deployed in Scotland
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.
