What will you do?
- Develop engineering solution to run production level machine learning and data-driven initiatives.
- Manage and monitor full lifecycle of ML models in production (e.g. monitor features, model results and performance)
- Schedule and orchestrating complex ML workflows and pipelines using latest technologies and schedulers. When all else fails, bash is something you script, and not action upon.
- Optimize the efficiency of machine learning algorithms by applying state-of-the-art technologies to reduce training time and inference latency.
- Work closely with data scientists, business and IT teams to build platform and framework to enable machine learning and data analytics activities on a large-scale.
- Continuous innovation and optimization of machine learning workflow, through R&D on new technologies.
- Establish, implement and maintain best practices and principles of machine learning engineering.
What you need to have:
- Bachelors in Computer Science, Computer Engineering, or in a highly related discipline.
- Excellent programming skills in at least one object-oriented programming language (Python, Java, C++)
- Bash, Shell, YAML, Ansible, Git, Maven, Jenkins, Junit, Ctrl-M, K8, Docker all makes sense to you.
- 5+ years of experience in software engineering or data engineering.
- Implementation experience in machine learning algorithms and applications.
- Strong expertise in ML model deployment tooling (including experience with tools for real production deployments, testing, management of package dependency, lineage/audit trails, model versioning), high performance computing and parallel data processing.
- Passionate about machine learning, new application areas and new tools
Nice to have:
- Experience working on Spark, HiveQL or Optaplanner is a plus.
- Knowledge in database modelling, big data or data warehousing concepts.
- Fluency in at least one modern distributed ML frameworks (TensorFlow, PyTorch, Caffe, MLFlow)
- Exposure in artificial intelligence – machine learning, deep learning, reinforcement learning.
- Experience working with Singaporean clients, the Singapore government, familiarity with GDPR in Europe, PDPA in Singapore will be an advantage
- Certification and applied experience in cloud-based analytics platforms such as:
- Microsoft Azure Analytics
- Amazon Web Services Analytics
- Google Cloud Platform Analytics