Data Scientist - ML frameworks and Infra

Key Skills

Apache Hadoop

Job Description

Job Description:

• Collaborate with product managers and data scientists to conceptualise, research, and build high performance and scalable machine learning solutions
• Develop infrastructure, tooling, and frameworks to streamline development and deployment of machine learning models
• Apply domain knowledge in machine learning and software engineering to contribute to various projects, including but not limited to:
• MLOps
• ML model training platform
• ML model serving

Minimum Requirements:

• Bachelor's Degree in Computer Science or related technical discipline
• In-depth understanding of computer science fundamentals including algorithms and data structures
• Experience in one or more programming languages (Go, C++, Java, Python)
• Experience in developing and deploying machine learning or other web backend services
• Experience in common machine learning frameworks (numpy, scikit-learn, xgboost, Tensorflow, PyTorch)

Preferred Requirements:

• Experience in building and optimising big data pipelines
• Experience in distributed databases or systems (Hadoop, Spark, HBase)

• Experience in Kubeflow or similar machine learning platforms
• Experience in one or more model serving frameworks (Tensorflow Serving, TorchServe, TensorRT, NVIDIA Triton Inference Server)

Minimum Qualifications:

• Bachelor's Degree, Master's Degree, or PhD in Computer Science or related technical discipline
• Minimum 2 years of relevant working experience for candidates with Bachelor's or Master’s Degree
• Experience with one or more programming languages including Python and Golang
• Familiarity with Linux Operating Systems
• Experience in developing and deploying live services

Preferred Qualifications:

• Proficient in SQL, building ETL data pipelines, and managing features in data warehouse / data lake
• Experience with distributed databases or distributed systems (Hadoop, Spark, HBase, Cassandra etc.
• Experience administering infrastructure and services in production environment
• Familiar with DevOps best practices
• Familiar with Docker and Kubernetes
• Experience deploying services in a multi-cloud environment
• Experience with Kubeflow or similar Machine Learning platforms
• Experience with common machine learning frameworks including scikit-learn, XGBoost, Tensorflow, or PyTorch
• Experience with Tensorflow Serving / TensorRT / TensorRT Inference Server
• Experience with front-end development frameworks and building admin console