This Data Scientist role sits within the Agentic AI Pod, focused on designing, building, and scaling agentic AI systems within Razer's internal AI platform. You will play a critical role in developing autonomous and semi-autonomous AI agents that combine large language models (LLMs), retrieval systems, fine-tuned models, and tool-based orchestration to enable intelligent, real-time capabilities across Razer's gaming and platform experiences.
The ideal candidate is a technically strong AI systems engineer with hands-on experience in agentic architectures, RAG pipelines, LLM fine-tuning, and production deployment. You will work across the full lifecycle-from data preparation and model adaptation to system integration, deployment, and continuous optimization-while collaborating closely with AI Software Engineers, Platform Engineers, and DevOps teams.
Key Responsibilities
- Design, implement, and maintain agentic AI architectures, including planning, tool use, memory, and multi-step reasoning
- Build, operate, and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, and internal knowledge sources
- Perform LLM fine-tuning and adaptation (e.g., supervised fine-tuning, instruction tuning, parameter-efficient methods such as LoRA) to improve task performance and domain alignment
- Develop internal frameworks, tooling, and orchestration layers for LLM-driven agents and workflows
- Integrate and adapt 3rd-party AI services (LLMs, speech, vision, agent platforms) into agent-based systems
- Evaluate, prototype, and productionize agent frameworks, models, and AI platforms, focusing on system performance, cost, and architectural fit
- Deploy and operate production-grade AI systems, addressing scalability, latency, reliability, observability, and cost controls
- Conduct benchmarking, evaluation, and trade-off analysis across models, fine-tuning strategies, agent behaviors, and retrieval approaches
- Collaborate with platform and infrastructure teams to ensure secure, compliant, and maintainable AI systems
- Stay current with advances in agentic AI, LLM fine-tuning techniques, RAG methods, and deployment patterns
Pre-Requisites
Technical Skills
- Minimum 2+ years of experience in AI systems engineering, agentic AI development, or applied ML in production
- Strong proficiency in Python and solid software engineering fundamentals (API design, testing, modular architecture)
- Strong proficiency in prompt design and prompt engineering for agentic AI systems (instruction design, role prompting, tool-use prompting, iterative refinement, and evaluation)
- Hands-on experience with LLM APIs (e.g., OpenAI, Claude, Gemini) and open-source LLMs
- Practical experience with LLM fine-tuning workflows, including data preparation, training, evaluation, and deployment
- Experience with agent and RAG frameworks such as LangChain, LlamaIndex, AutoGen, or similar
- Experience deploying and operating AI systems with attention to latency, throughput, and reliability
- Familiarity with cloud platforms (AWS, GCP, Azure) and AI deployment / MLOps workflows (CI/CD, monitoring, versioning)
Preferred Qualifications
- Experience with parameter-efficient fine-tuning (PEFT) techniques such as LoRA, QLoRA, or adapters
- Hands-on experience with vector databases (e.g., Pinecone, Weaviate, Milvus, FAISS)
- Strong understanding of prompt engineering, retrieval strategies, and RAG evaluation
- Experience operating and debugging agent-based systems in production
- Ability to clearly communicate architectural decisions and trade-offs
- Passion for gaming and interest in intelligent, interactive AI experiences
- Comfortable working in a fast-paced, high-pressure, agile environment
Education & Experience
- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical discipline
