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 :

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.

Pre-Requisites :

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?