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.
The AI Engineer 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 lifecyclefrom data preparation and model adaptation to system integration, deployment, and continuous optimizationwhile collaborating closely with AI Software Engineers, Platform Engineers, and DevOps teams.
As a Senior, you will take a technical leadership role and own complex system designs end-to-end, drive architectural decisions, mentor other engineers, and influence the technical roadmap for agentic AI.
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
