Job Responsibilities
- Design, develop, and deploy AI‑powered solutions.
- Build scalable AI systems and integrate AI / Machine Learning (ML) models into applications, optimizing AI workflows for production.
- Collaborate closely with data scientists, software engineers, and DevOps teams to bring cutting‑edge AI capabilities to life.
- Implement and optimize AI / ML models in production via containerization (Docker, Kubernetes) for speed, accuracy, and cost‑effectiveness.
- Deploy AI models using RESTful APIs, gRPC, or edge computing solutions.
- Implement monitoring, logging, and error‑handling mechanisms to track AI performance.
- Design APIs and microservices to integrate AI capabilities into software systems, ensuring they are secure, performant, and well‑documented.
- Monitor and optimize model performance in production environments, ensuring API latency and availability meet the required SLAs.
- Build scalable, high‑performance AI pipelines for real‑time and batch processing.
- Implement MLOps best practices, including CI/CD for machine learning workflows on both local machines and cloud platforms.
- Work closely with data scientists to turn research models into deployable solutions.
- Collaborate with DevOps and cloud engineers to ensure seamless AI model deployment.
- Stay up to date with the latest AI/ML frameworks, tools, and industry trends in API and plugins development to continuously improve and enhance API solutions.
- Identify opportunities to improve existing AI API / plugins architectures and suggest new features or services to add value to AI products.
Pre‑Requisites
- 3+ years of experience in AI/ML software development.
- Proven track record of deploying AI solutions in production environments.
- Strong experience in deploying AI models and API development using RESTful APIs, gRPC, or edge computing solutions.
- Strong programming skills in Python, Java, or C++.
- Hands‑on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- In‑depth understanding of containerization (Docker, Kubernetes) and DevOps practices.
- Experience developing and deploying AI applications for edge deployment (TensorRT, ONNX, TFLite) and on cloud infrastructure (AWS, Azure, or Google Cloud Platform) using Infrastructure as Code tools such as Terraform and cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML).
- Experience with database management (SQL, NoSQL, and vector databases).
- Knowledge of distributed computing (Spark, Dask) and parallel processing.
- Ability and willingness to learn new technologies and apply them at work in a fast‑paced, high‑pressure, agile environment.
- Excellent written and verbal communication skills for coordinating across teams.
Education & Experience
Has a Bachelor’s or Master’s degree in computer science, AI or a similar discipline from an accredited institution.
Travel Requirements
Role based in Singapore office and may require up to one travel trip per year.
Equal Opportunity Employer
At Razer, we believe diverse teams build better ideas and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee. We do not discriminate on the basis of race, ethnicity, color, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected by the laws of the countries we operate in. We also provide reasonable accommodations where needed — including for disability or religious practices — so every team member can perform and contribute at their best.