Senior Data Scientist

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.

We are seeking a highly skilled and innovative Data Scientist to join our software team, leveraging user configuration data and software configuration schemas to fine-tune large language models (LLMs) in the 8B–32B parameter range. You will build an AI-powered configuration assistant that combines LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG) with VectorDB & GraphDB, and model optimization (including quantization) to deliver accurate, fast, and cost-effective recommendations to users.

This is a full-stack applied AI role, covering data handling, model training, deployment, monitoring, and optimization in production.

Key Responsibilities

1. LLM Fine-tuning & Evaluation

• Fine-tune and adapt LLMs for domain-specific configuration assistance.
• Apply instruction tuning, LoRA, RLHF, and domain adaptation.
• Establish automated evaluation pipelines for accuracy, latency, and safety.

2. Prompt Engineering

• Design, test, and optimize prompt strategies for varied scenarios, personas, and workflows.
• Develop reusable prompt templates and dynamic context injection logic.
• Run A/B tests to measure prompt impact on user outcomes.

3. Retrieval-Augmented Generation (RAG) with VectorDB & GraphDB

• Implement semantic retrieval with VectorDB (e.g., FAISS, Pinecone, Weaviate).
• Build GraphDB (e.g., Neo4j, TigerGraph) pipelines to represent and query configuration relationships.
• Combine embedding search with graph reasoning for richer context in LLM outputs.
• Optimize retrieval for both latency and relevance.

4. Model Quantization & Optimization

• Apply quantization, pruning, and distillation to right-size LLMs for deployment.
• Benchmark trade-offs between quality, speed, and cost across CPU/GPU/edge.
• Collaborate with infrastructure teams on inference optimization.

5. Data Handling & Engineering

• Extract, clean, and structure configuration and schema data (JSON, YAML, XML).
• Proficiency with SQL for querying and transforming relational datasets.
• Build automated pipelines for continuous retraining and RAG index updates.
• Apply schema-aware data modeling for improved retrieval and training.

6. Production Deployment & Monitoring

• Collaborate with software engineers to integrate AI into live products.
• Develop APIs and microservices for LLM-powered features.
• Set up monitoring dashboards, drift detection, and feedback loops.
• Implement safety guardrails to prevent hallucinations and unsafe recommendations.

7. Security, Privacy & Compliance

• Ensure compliance with data privacy regulations (e.g., GDPR, SOC 2).
• Apply data anonymization and access control practices.
• Design output filtering to avoid sensitive or incorrect recommendations.

Requirements

Must-Have:

• 3+ years in Data Science, ML, or NLP with hands-on LLM fine-tuning experience.
• Proven skills in prompt engineering and RAG pipeline development.
• Experience with VectorDB and GraphDB integration.
• Hands-on experience with model quantization and optimization.
• Proficiency in Python (Hugging Face Transformers, PyTorch, LangChain).
• Proficiency with SQL and relational data modeling.
• Knowledge of YAML, JSON, XML, and schema-based data structures.
• Strong grasp of MLOps principles for production deployment.

Preferred:

• Experience with GPU optimization tools (ONNX Runtime, TensorRT).
• Background in software configuration management systems.
• Familiarity with CI/CD, Docker, Kubernetes for ML services.
• Experience in LLM evaluation frameworks (e.g., Ragas, HELM, OpenAI Evals).

Are you game?