Leads data and analytics consulting for customers across industries, defining AI and data-use strategies, designing integrated solutions, and guiding adoption roadmaps while mentoring consultants.
About this role
This application systems specialist sits in NEC’s financial solutions organization, which plans, proposes, develops, integrates, and maintains systems for major trust banks and their group companies. The application team has about 20 members and works on distributed systems for large financial institutions.
The role leads business-application projects using an application framework built for a major trust bank. Work spans requirements and design documents, implementation, test planning, test specifications, reviews, and proposals for host-migration projects. Technologies named in the posting include SPA, microservices, cloud, containers, Spring Framework, MyBatis, JavaScript, TypeScript, CSS, and npm. The development lead works with partner-company members and guides the team.
Applicants must have about three years of application-development experience. The 主任 track also requires customer-negotiation experience; the 担当 track does not require financial-industry experience or a specific development language.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong. Always confirm everything on the official posting before applying.
Similar jobs
Lead financial DX and AI implementation for NEC/Painter partners, designing cloud systems, deploying LLM-based AI agents, aligning business goals, and building the engineering team.
Lead retail-system projects from requirements definition through development, testing, deployment, and operations, coordinating customers and project teams while applying cloud, web, AI, and digital expertise.
Lead server-side development and technical decisions for Loglass SaaS, including rearchitecture, performance improvements, workflow automation, and project leadership. Telework is included.
Provide senior technical support for Red Hat Enterprise Linux in hybrid-cloud environments, resolve complex customer issues, share knowledge, and coordinate with developers and QE to improve the product.
Design and implement machine-learning algorithms using tabular, natural-language, and image data, then work with consultants and software engineers to turn client solutions into usable software.