Machine Learning Engineer

Requirements

• Ideal academic background would be a BSc or MSc degree in computer science, software engineering, or information technology.
• Proficiency with Spark, SQL, Python or Scala.
• Experience in Software development life cycles: code, unit tests, git or other SCM, CI/CD pipelines, monitor logs or service resource usages.
• Basics knowledge in computer engineering: computer systems, network, security.
• Understand common machine learning concepts: "features," "metrics," decision trees, gradient boosting, embedding, neural networks, etc. Underlying rigorous mathematical proofs are not required, but ML Engineer needs to understand what a model does to adjust it.
• Communication and interpersonal skills to work with other scientists/engineers and non-technical business units.
• Work Experience as ML Engineer, Data Engineer, Fullstack or Backend Software Engineer, Solution/Cloud Architect or related roles.
• Experience with these libraries is a plus: TensorFlow, MLFlow, SynapseML, TensorRT, ONNX, django, react, etc.
• Experience working with cloud, preferably Azure.
• Data privacy, PDPA awareness
• English Proficiency. Most specs and documents are written in English.
• We're committed to bringing passion and customer focus to the business.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us.

Additional Information

สมัครโปรดคลิกที่ปุ่ม "Apply" If you are an active SCB employee, please apply through Workday by searching "Find Jobs". If this is your first time applying you will need to create a candidate account when you click on apply. Job Description Productionize models: architectural design, develop, test, deploy the whole software components as microservices, Databricks workflows, ADF pipelines, etc. to serve in high load environments. Work closely with data scientists to track performance, set up model monitoring, automated alerts, and further optimize features/models. You will be working on data pipelines and ETL of thousands of tables. Ingesting data from various sources: Azure Blob, sFTP, Kafka, SQL Server, REST API. Running models and integrate into several destinations: Azure Blob, S3, sFTP, Kafka, Databases, emails, MS teams notifications, etc. Further enhance and maintain our in-house platform for managing and automating Features and Models. This involves full stack site, Databricks, job scheduling tools, helper library, and a lot of problem-solving skills.

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Company Intel

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