锦鲤求职岗位描述
Build core components for Document Intelligence to enable enterprise AI use cases, delivering standardised, robust, and scalable solutions that meet growing business demand. Develop and maintain scalable APIs and integration services to support AI products, workflows, and platform capabilities end-to-end. Contribute to architecture and technical governance by conducting design/architecture reviews and enforcing engineering standards, coding practices, documentation, and quality assurance. Deliver hands-on engineering including API development, unit/integration testing, defect resolution, and partnering with testers on test cases and test data preparation. Able to communicate with stakeholders across systems (upstream/downstream) to define requirements and design effective, well-governed API interfaces. Strengthen DevOps and engineering processes to improve delivery and support efficiency while ensuring adherence to established standards. Deploy and scale on cloud platforms by leveraging GCP and Kubernetes to manage applications effectively in enterprise environments. Ensure reliability, performance, and compliance through monitoring, load/performance testing, optimisation, and incident response to maintain low latency, high throughput, and stable operations (including onsite/offsite production support). Act as a senior individual contributor with strong solution design and hands-on coding expertise, demonstrating excellent technical problem-solving as well as strong analytical, communication, and interpersonal skills. Hold a university degree in Computer Science or a related discipline. Bring 7+ years of IT experience (preferably in enterprise application or platform development) with 4+ years' hands-on proficiency in Java and Python for backend and platform engineering. Show exposure to all stages of the Software Development Life Cycle (SDLC) and a solid working knowledge of Agile methodology. Knowledge of Docker, Kubernetes, micro-frontend architecture, and LLM new solutions a strong advantage. Experienced in leveraging coding assistants (e.g., AI pair-programming tools) to accelerate software development, enhance code quality, and support engineering best practices.