Machine Learning Operations Engineer

Are you a software developer looking to debunk Machine Learning myths? NCS is looking for you to join us as an MLOps engineer, where you will be designing and developing production grade codes catered to operational machine learning workflows. You take joy in ensuring the models are running efficiently, and take pride that no edge cases can derail your setup. You love building connectors to standardise highly stackable services, and desire to attain self-learning artificial intelligence. If this resonates well with you, bring along your Git library and development machine when we have a chat.

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.
• 2+ 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