Build and operate the Marketplace data warehouse end to end, covering batch and real-time ingestion, semantic layer design, and application-layer modeling for downstream consumers.
Partner with vendors and internal stakeholders to design, develop, and enhance the Synapxe Observability Platform, including monitoring, logging, metrics, distributed tracing, visualization, and AI/ML functionalities.
Develop, validate and deploy new software components (for example advance software security mechanism and next generation IoT connectivity protocols) for embedded operating systems targeted for mobile and IoT devices, in applications such as mobile payment, digital identity and IoT connectivity
About the job We are seeking an experienced Machine Learning Engineer to join a fast-growing technology company, building and scaling machine learning capabilities across its products and platforms. This is an opportunity to work at the intersection of machine learning, software engineering, and cloud infrastructure, taking ML solutions from experimentation through to reliable, scalable production deployment. Responsibilities You will design, develop, and productionise machine learning models and services, working closely with data scientists, software engineers, and product teams to integrate ML capabilities into technology products. You will build and maintain MLOps infrastructure and automated ML pipelines covering model training, testing, deployment, monitoring, and retraining. You will establish CI/CD practices for machine learning, improve model reliability and performance, and develop scalable infrastructure to support models in production. You will also contribute to ML platform architecture, model serving, observability, and automation, ensuring machine learning systems are scalable, secure, and efficient. Requirements We are looking for an experienced Machine Learning Engineer with 6-10 years experience , strong software engineering fundamentals and hands-on experience deploying and operating machine learning models in production. Strong proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or similar technologies are essential. You should have practical MLOps experience, including model deployment, CI/CD, model monitoring, experiment tracking, containerisation, and automated training and inference pipelines. Experience with Docker, Kubernetes, cloud platforms (AWS, Azure, or GCP), and MLOps technologies such as MLflow, Kubeflow, or equivalent tools will be highly relevant. Experience building scalable APIs, model serving infrastructure, and production ML platforms would also be advantageous. To apply Interested candidates, please send your resume to Eliza Ng at en@kerryconsulting.com, quoting the job title. Due to the high volume of applications, only shortlisted candidates will be notified. L/N 16S8060R/N R1549515
Develop advanced packaging technology platforms (fan-out packaging, 2.5D interposer and 3D stacking) capabilities and novel process integration approaches for heterogeneous chiplets integration.
Collaborate with technical vendors and internal stakeholders to provide maintenance and support of MS Dynamics solutions, Secure File Transfer including patching, security remediation, log review, ensuring alignment with client requirements and timelines.
Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.