Senior Data Scientist (Generative AI)

Razer is hiring a Senior Data Scientist (Generative AI) in Singapore, Singapore. Level rates it ; you can apply on Razer.

Level's read on this job

On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include PyTorch, TensorFlow and SageMaker.
Level classifies it as a senior role.
Level lists 148 other open roles at Razer.

AI in this role

pytorchtensorflowsagemaker

prompt-engineeringragai-agentsfine-tuning

Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.

Job Responsibilities :

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.

Essential Duties and 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.

Pre-Requisites :

Qualifications

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.

Preferred 

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.

Education & Experience

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).

Travel Requirements

Role based in Singapore office, with occasional travel (up to 1 trip per year) for conferences, research collaborations, or business meetings.

Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee across all the countries we operate in. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected under local laws. Where needed, we provide reasonable accommodations - including for disability or religious practices - to ensure every team member can perform and contribute at their best.

Are you game?

How we rate this

Senior Data Scientist (Generative AI) at Razer rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Data Scientist (Generative AI) is an open job at Razer.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Singapore.
The job was posted on September 18, 2026.
Seniority: senior.
Applications are handled by Razer. Apply on Razer

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGAI agentsFine-tuningPyTorchTensorFlowSageMaker

Also common in data scientist jobs at this level

SQL Spark NLP scikit-learn MLOps Azure

Pay

Typical data scientist job: $167k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, AI agents, Fine-tuning, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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