Machine Learning Engineer

Job Summary

ML Engineer in SCB Data Analytics team is a unique combination of Data Engineer, MLOps, and a bit of Full Stack. You will be responsible for many engineering tasks from integrating incredible amount of data in SCB, to developing and maintaining a production grade software solution to leverage the data and deliver values to the bank and its millions of customers.

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.

Qualifications

• 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.

Preferred Qualifications

• 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.