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
- 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)
- 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
