Data Scientist
18 hours ago
This Data Scientist role sits within the Applied AI Pod, focused on evaluating, integrating, and scaling 3rd-party AI tools into Razer’s internal AI stack. You will play a critical role in building robust, scalable systems that bridge external AI solutions with our internal infrastructure to enable intelligent, real-time capabilities across gaming and platform experiences. The ideal candidate is a technically strong generalist with a strong grasp of applied AI/ML, experience working with external AI APIs (e.g., GenAI, vision, speech) and skilled in implementing and adapting AI solutions into production-grade systems. You will collaborate closely with AI Software Engineers, Data Scientists, and DevOps engineers to extend Razer’s AI capabilities through reliable, high-performance integration work. Job Description Build and maintain internal tooling, pipelines, and wrappers to integrate external AI services (LLMs, speech, vision, agents) into Razer's ecosystem Develop and optimize lightweight in-house AI/ML models to enhance and complement 3rd-party tools Evaluate and prototype external AI solutions, focusing on code-level integration, architecture compatibility, and system performance Conduct benchmarking, trade‑off analysis, and technical validation for external APIs and platforms Collaborate with software engineering and DevOps to ensure secure, production‑ready, and scalable deployments Stay up to date with the latest developments in AI SDKs, APIs, toolchains, and deployment strategies Prerequisites Technical Skills Minimum 1 year of hands‑on experience in applied AI/ML, AI systems engineering, or AI integration Strong proficiency in Python and software engineering fundamentals, including experience with API design, data structures, and modular codebases Proven experience working with LLM APIs (e.g., OpenAI, Claude, Gemini), AI SDKs, and toolchains such as LangChain, Hugging Face, or RAG frameworks Experience integrating external AI APIs into internal platforms with attention to latency, throughput, and reliability Familiarity with cloud environments (AWS, GCP, or Azure) and MLOps workflows (e.g., model deployment, versioning, CI/CD for ML systems) Preferred Qualifications Exposure to vector databases, prompt engineering, or agent‑based architectures is a plus Strong ability to debug, test, and validate AI services in production environments Ability to clearly communicate technical decisions and trade‑offs across engineering and AI teams Passion for gaming and interest in using AI to enhance user experiences is a plus Comfortable working in fast‑paced, high pressure, agile environment. Education & Experience Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline #J-18808-Ljbffr
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