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Singapore
Onsite
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.
Lead AI Data Engineering initiatives by driving the design and development of robust data pipelines for AI/ML workloads, ensuring efficiency, scalability, and reliability.
Design and implement data architectures that support AI model training, including feature stores, vector databases, and real-time streaming solutions.
Develop high performance data pipelines that process structured, semi-structured, and unstructured data at scale, supporting the various AI applications
Implement best practices for data quality, lineage, security, and compliance for AI applications
Develop automated data workflows, integrate with DevOps, MLOps frameworks, and enable model reproducibility through efficient data management.
Continuously explore advancements in AI data engineering, including distributed computing, data mesh architectures, and next-gen storage solution that best fit the various AI initiatives
Work closely with data scientists and AI/ML engineers to optimize feature extraction, data labeling, and real-time inference pipelines
Hands on experience working with Vector/Graph;Neo4j
3+ years of experience in data engineering, working on AI/ML-driven data architectures
Ability to work in fast paced, high pressure, agile environment.
Ability and willingness to learn any new technologies and apply them at work in order to stay ahead.
Strong in programming languages such as Python, SQL.
Experience in developing and deploying applications running on cloud infrastructure such as AWS, Azure or Google Cloud Platform using Infrastructure as code tools such as Terraform, containerization tools like Dockers, container orchestration platforms like Kubernetes
Experience using orchestration tools like Airflow or Prefect, distributed computing framework like Spark or Dask, data transformation tool like Data Build Tool (DBT)
Excellent with various data processing techniques (both streaming and batch), managing and optimizing data storage (Data Lake, Lake House and Database, SQL, and NoSQL) is essential.
Experience in network infrastructure, real-time AI inferencing pipelines using event-driven architectures
Excellent problem-solving and analytical skills, with an understanding of Gen AI technologies and their applications
Excellent written and verbal communication skills for coordinating across teams.
Experience and Interest to keep up with the advancement in the Data Engineering Field
Education & Experience
Has a Bachelor’s or Master’s degree in computer science, Data Engineering, AI/ML, or a related field from an accredited institution
Travel Requirements
Role based in Singapore office and may require up to 1 travel trip per year
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