{"id":3607074,"url":"https://alion.io/job/jpmorganchase-lead-software-engineer-platform-ai-acceleration-4","title":"Lead Software Engineer - Platform AI Acceleration","company":{"id":257,"name":"JPMorganChase","domain":"jpmorganchase.com","url":"https://alion.io/company/jpmorganchase","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":89,"open_postings":1107,"ghost_share":0.02,"stale_share":0.578,"repost_share":0.051,"time_to_fill_p50_days":14,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Glasgow, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"CloudFormation","optional":false},{"name":"Copilot","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Terraform","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-09-15T15:16:00Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-05T04:54:19Z","board_verified":true,"closed_at":null,"days_open":50,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":50},"description":"As a Lead Software Engineer at JPMorgan Chase within Infrastructure Platforms Applied Artificial Intelligence and Machine Learning (AI/ML), you will join a small, specialist team responsible for creating horizontal capabilities, including reusable APIs, libraries, reference architectures, and practical enablement that accelerates delivery across key products and platforms.\nWe are seeking a Lead Software Engineer to join a small, specialist team responsible for creating horizontal capabilities, including reusable APIs, libraries, reference architectures, and practical enablement that accelerates delivery across key products and platforms. This is a hands-on technical specialist role, with the opportunity to create new systems and capabilities that will accelerate our business.\nThe ideal candidate brings a strong foundation in software engineering and AI/ML, along with proven experience leading the development and production operation of AI-enabled systems in secure, enterprise environments. In this role, you will collaborate closely with engineering teams across our group to develop priority use cases, design and build services, and promote best practices for scalable, resilient, and secure AI adoption. You will also mentor engineers, contribute to firmwide standards and thought leadership, and help ensure the organization stays at the forefront of AI engineering advancements.\nYour deep experience of developing and deploying software to production in enterprise environments will be key to enabling our AI strategy, combining practical expertise with AI/ML knowledge to ensure our systems are secure, compliant and performant.\nJob responsibilities\nDesign and develop modular, maintainable AI systems and interfaces aligned to Infrastructure platform standards, optimizing for scalability, resiliency, and long-term operability.\nPartner closely with Applied AI/ML, Data Science and Cybersecurity engineers to develop secure AI solutions for Infrastructure Platforms and our consumers.\nCreate secure and high-quality production code, and reviews and debugs code written by others\nEvaluate AI solution approaches from engineering standpoint to improve overall design quality, code quality, and operational outcomes.\nInfluence peers and project decision makers to adopt leading-edge technologies where they create measurable value.\nDrives team adoption of enterprise AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team\nContribute to the engineering community by advocating firmwide frameworks, tools, and practices for the AI-enabled System Development Life Cycle (SDLC).\nFoster a team culture of opportunity, inclusion, and respect.\nRequired qualifications, capabilities, and skills\nSignificant hands-on system/software development experience, delivering production services in secure, regulated environments.\nDemonstrated experience leading technical discussions (technical direction, design reviews, quality standards, and operational readiness) across a team or program.\nSignificant experience in at least one modern programming language: Java, Python.\nExperience using AI coding assistants (GitHub Copilot, Claude Code etc) to complete software engineering tasks.\nSolid understanding of building AI solutions using an LLM backed architecture.\nStrong understanding of API design, microservices, and event-driven architectures.\nExperience building and operating cloud-native services on AWS, including Bedrock. \nStrong DevOps practices: CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code (e.g., Terraform/CloudFormation).\nStrong prompt engineering capability, including system prompts, few-shot prompting, tool/function calling, and structured outputs (e.g., JSON schemas).\nDemonstrated experience driving secure and effective adoption of enterprise-approved AI-assisted engineering tools (coding, review, testing, troubleshooting) and setting team expectations for validating AI outputs for correctness, performance, and 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