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AI Lead Solutions Engineer -Forward Deployed

JPMorgan Chase
Glasgow, GBR
Full-time
Lead
Onsite
Discovered 2 weeks ago
awspython

Full Job Posting

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

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