oracle
Lead Software Engineer - AI/Java Full Stack
J.P. Morgan
Glasgow, GBR
Full-time
Senior
Onsite
Discovered 2 weeks ago
JavaSpring BootSQLReactDockerKubernetes
Free
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JavaSpring BootSQL
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JavaSpring BootSQLReactDockerKubernetes
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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)
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