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.

**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.
- 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**
- 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.

**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).
- 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?