Job description
Senior Machine Learning Engineer/ Machine Learning Engineer at Dkatalis Singapore
Job Specification:
We require machine learning engineers to work together with the data science team to enable them to rapidly design, build, train, optimise, deploy and monitor models into production environments. The data scientists should focus on the data analysis, feature engineering and model design, while the machine learning engineers should focus on providing and running the infrastructure and tooling to ensure reliable deployment and operation in production.
You will need to build and operate the infrastructure to enable full cycle Machine Learning Ops. If you don’t know what a feature store is nor what training-serving skew is, then don’t apply. Models will need to support both online and offline inference and will be integrated into customer facing product features requiring 99.9% uptime. The models will support a number of business capabilities including:
- digital banking product features such as smart financial recommendations around how much to save and when
- Business operations optimisation
- Growth, go-to-market and customer engagement
- Fraud detection and other risk management functions
- Improving the efficiency of various technical operations with the business
You will also work closely with data engineering, product, software engineering and devops. A mature approach to being able to balance technical and business concerns in a pragmatic manner that respects long term business objectives is required.
Exposure to retail banking is desirable.
Job Requirements:
- A computer science / informations / engineering graduate degree with a strong background in mathematics and statistics
- A sound demonstration of both the theoretical foundations underpinning machine learning and deep learning models as well as hands on experience dealing with the problems they throw up in the real world.
- 3+ years software engineering experience with at least one year deploying machine learning models in production environments at scale
- 3+ years experience with big data / distributed processing systems
- Strong familiarity with tools within the PyData ecosystem such as Numpy, Scipy, pandas, scikit-learn, PyTorch, Tensorflow
- Experience with cloud technologies is a huge plus (especially GCP)
- Experience with Kubernetes
- Experience with Kubeflow is highly desired
- Experience with at least one MLOps framework such as TFX, Flyte, etc or at the very least a homegrown solution
