JPMorganChase provides banking, financing, asset management and payment processing services to individuals, businesses, institutions and governments.
Lead Software Engineer - Java Full Stack & AI Solutions - Corporate Data & Analytics Service
En tant que Lead Software Engineer chez JPMorganChase à Glasgow, vous concevrez et développerez des solutions logicielles sécurisées et évolutives au sein de Corporate Technology. Vous développerez des applications d’IA et des solutions de données à grande échelle avec Java et Scala, sur AWS, Databricks et Kubernetes. Le poste comprend des décisions d’architecture, l’amélioration de la stabilité des plateformes et l’accompagnement d’autres ingénieurs.
- Level : Lead / expert
- Schedule : Full-time
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Java
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Scala
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Spring Boot
- SQL
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React
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AWS
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Databricks
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Kubernetes
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Docker
- ECS
- EKS
- REST
- GraphQL
- RAG
- LangChain
- LlamaIndex
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ElasticSearch
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Oracle
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MongoDB
Information about JPMorganChase
- Official domain
- jpmorganchase.com
- Open jobs
- 395
Job details
The full description published by JPMorganChase.
Description de l’offre
We have an exciting opportunity to advance your career and drive meaningful impact by pushing the limits of modern engineering. As a
Lead Software Engineer at JPMorganChase within Corporate Technology, you will be a key technical leader within an agile team responsible for designing, building, and delivering secure, scalable, and high-performing technology solutions. You will contribute across multiple domains, partnering with business and technology stakeholders to deliver strategic platforms that enable the firm’s objectives.
Job Responsibilities
- Lead the design, development, and delivery of innovative software solutions, solving complex problems with creative and forward-thinking approaches
- Build and maintain high-quality, secure, and production-grade code, while mentoring and reviewing the work of other engineers
- Drive architecture decisions to ensure scalability, resiliency, and zero-downtime deployments
- Identify opportunities to automate operational processes and eliminate recurring issues, improving platform stability and efficiency
- Develop and implement data-intensive and statistical solutions using Java/Scala on large-scale datasets
- Design and build AI-enabled applications, including AI Agents and agentic workflows
- Work across hybrid data ecosystems (AWS, Databricks, Kubernetes)
- Ensure code quality through unit testing, integration testing, and continuous validation practices
- Collaborate with cross-functional teams to deliver end-to-end solutions aligned with enterprise standards
Required Qualifications
, Capabilities, and Skills
- Significant experience in software engineering with formal training or equivalent practical expertise
- Strong proficiency in Java and Spring Boot, with working knowledge of SQL and modern front-end frameworks (e.g., React)
- Demonstrated experience in designing scalable, secure, and resilient distributed systems
- Strong understanding of modern architecture patterns (microservices, event-driven, API-first)
- Experience with CI/CD pipelines, containerization (Docker, Kubernetes/ECS/EKS), and Infrastructure as Code
- Expertise in API design (REST, GraphQL) and integration patterns
- Hands-on experience building applications on AWS or other public cloud platforms
- Experience with Generative AI systems, including:
- RAG (Retrieval-Augmented Generation) architecture
- AI orchestration frameworks (LangChain, LlamaIndex, etc.)
- LLM evaluation frameworks and responsible AI practices (guardrails, content filtering) Preferred
Qualifications
, Capabilities, and Skills
- Deep expertise in Java, object-oriented design, and system design
- Experience with ElasticSearch, Oracle, MongoDB, and modern data platforms
- Proven experience building AI-enabled applications and agentic architecture
- Experience developing reusable AI Agent skills/frameworks to accelerate engineering teams.
Requirements
- Expérience significative en génie logiciel
- Maîtrise de Java et Spring Boot
- Connaissance de SQL et des frameworks front-end modernes
- Expérience en systèmes distribués évolutifs, sécurisés et résilients
- Expérience avec CI/CD, conteneurs et Infrastructure as Code
- Expérience pratique sur AWS ou une autre plateforme cloud publique
- Expérience des systèmes d’IA générative, notamment RAG
- Connaissance des frameworks d’orchestration IA
- Connaissance des cadres d’évaluation LLM et des pratiques d’IA responsable
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