oracle
AI Lead Solutions Engineer -Forward Deployed
JPMC Candidate Experience page
Glasgow, GBR
Full-time
Senior
Onsite
Discovered 2 weeks ago
PythonClaude CodeGitHub CopilotCI/CDcontainerisation
Free
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PythonClaude CodeGitHub Copilot
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PythonClaude CodeGitHub CopilotCI/CDcontainerisation
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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
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