Systems Analyst (Cloud-Native)
# Systems Analyst (Cloud-Native) **Edps Systems Limited** · Hong Kong · `On-site` 🕒 **Статус:** *Опубликовано: 2 дня назад* · *Источник: Indeed* --- ### About the Role About Us EDPS Systems Limited is a Hong Kong-based company, now developing its own in-house AI products. We are building a dedicated team to design, build and run these products in the cloud. The role You will work on the end-to-end integration of our in-house AI products, bringing devices, data pipelines, APIs, databases, AI/ML services and web and mobile apps together on one cloud-native platform. This is a hands-on role: you will design, build, deploy and operate cloud-native services, and keep them secure, scalable and well documented. You will work closely with our software engineers, data scientists and external technology partners. Key responsibilities Design and deliver the end-to-end integration architecture for our AI products, from data ingestion through cloud APIs, data stores and ML services to web and mobile applications. Build cloud-native services on AWS using microservices, containers (Docker, ECS/EKS) and serverless components (e.g. Lambda, API Gateway), designed for scalability, resilience and cost efficiency. Automate infrastructure and releases with infrastructure-as-code and CI/CD pipelines. Build and maintain RESTful APIs, event-driven messaging and ETL pipelines across NoSQL and relational databases. Integrate AI/ML components, such as model serving and document-processing services, taking them from prototype to production. Set up and manage development, staging and production environments, and own observability (monitoring, logging, alerting), backup and incident response. Implement cloud security controls for sensitive personal data: role-based access, encryption, audit trails and PDPO compliance. Integrate third-party devices and vendor systems, and write integration specifications, API documentation and system integration documents. Provide technical guidance to engineers, review pull requests and integration designs, and contribute to sprint planning and technical risk assessment. Requirements Degree in Computer Science, Computer or Electronic Engineering, Information Technology or a related discipline. Minimum 8 years of hands-on system integration experience (8-9 years preferred), including end-to-end delivery of multi-component platforms. MUST: Proven Cloud-Native development and deployment experience , having designed, built and deployed production workloads on AWS (preferred), Azure or GCP using microservices, containers and serverless services. Strong with Docker, Kubernetes or ECS/EKS, CI/CD pipelines (e.g. GitHub Actions, Jenkins) and infrastructure-as-code (Terraform, CloudFormation or CDK). Solid backend development in Python and/or Node.js or Java; RESTful API design; event-driven architecture and messaging (e.g. MQTT, Kafka, SQS/SNS). Hands-on with MongoDB and PostgreSQL (or equivalents), and with building data pipelines and ETL. Good knowledge of cloud security and observability: IAM, network segregation, encryption, secrets management, monitoring, logging and audit. Able to write clear technical documentation, such as API specifications and system integration documents. Good command of written and spoken English and Chinese (Cantonese); Mandarin is an advantage. Advantageous - IoT device integration and real-time data streaming. - Putting ML models into production (MLOps, model serving, AWS SageMaker) and using cloud AI services such as document AI. - Data and ML pipeline tooling: Apache Airflow for workflow orchestration and MLflow for experiment tracking, model registry and deployment. - Managed cloud data services (e.g. MongoDB Atlas, data lakes). - Experience in healthcare or other domains handling sensitive personal data. - AWS Certified Solutions Architect or DevOps Engineer (Professional), Kubernetes (CKA/CKAD) or equivalent; exposure to ISO 27001. - Agile/sprint delivery, mentoring engineers, or working in a start-up or incubator environment. Full-time
Наблюдалась 2026-10-06, впервые 2026-10-04, источник — Indeed.