Senior Data Scientist (Generative AI) at Razer in Singapore

Job description

• This role will focus on designing and developing GenAI-driven solutions for interactive systems and developer-facing tools. The ideal candidate will have strong expertise in GenAI technologies, including large language models, retrieval-augmented generation (RAG), and agentic workflows, with experience building scalable and production-ready AI services. This role involves working closely with data engineers, software engineers, and game developers to deliver robust AI tools, manage production pipelines, and enable fast iteration in real-world environments.

Responsibilities

• Design, develop, and maintain GenAI solutions for developer tools and interactive applications.
• Build and extend RAG-based systems, including retrieval pipelines, indexing strategies, and prompt orchestration.
• Develop agentic workflows and GenAI packages to support reasoning, planning, and tool-based execution patterns.
• Collaborate with software engineering teams to design and manage production pipelines for GenAI microservices.
• Work with data engineering teams to define data schemas, ingestion pipelines, and validation strategies for structured and unstructured data used by GenAI systems.
• Fine-tune and adapt large language models for domain-specific tasks and performance requirements.
• Partner with AI engineers to evaluate GenAI system performance using task-specific metrics, benchmarks, and qualitative analysis.
• Optimize GenAI services and pipelines for latency, scalability, reliability, and cost efficiency in production environments.
• Assist in the deployment, monitoring, and maintenance of GenAI services in cloud-based infrastructures.
• Document system designs, workflows, experiments, and implementation details for internal knowledge sharing.
• Stay updated on emerging trends and advancements in GenAI, LLMs, and agent-based systems through research and experimentation.
• Consider ethical, legal, and regulatory implications in the development and deployment of AI systems.

Requirements

• Proven experience in developing and deploying applied machine learning or Generative AI systems in real-world applications.
• Proficiency in Python; experience with additional languages is a plus.
• Strong understanding of LLMs, prompt engineering, retrieval systems, and agent-based architectures.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience building and evaluating RAG pipelines, GenAI tools, or AI-powered microservices.
• Familiarity with cloud-based AI platforms and production ML workflows for training, evaluation, and deployment.
• Ability to evaluate and optimize model performance using task-specific metrics and benchmarks.
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills across technical and non-technical teams.
• Master’s or PhD in a relevant field (Computer Science, AI, Machine Learning, etc.).
• 2+ years of applied experience in machine learning or Generative AI (academic or industry).
• Experience building RAG microservices, agent frameworks, or modular GenAI tooling.
• Exposure to multi-GPU training, distributed experiments, or large-scale model fine-tuning.
• Experience with AWS SageMaker or similar managed ML platforms.
• Awareness of emerging GenAI techniques, including tool-using agents, function calling, and planning-based workflows.

Skills

Required

• Proficiency in Python