Machine Learning DevOps Engineer
Our client, a global banks IT team (including Compliance IT) has started a very ambitious programme or work to aggressively migrate data & analytics onto Google Cloud Platform (GCP). This role is needed to support the Compliance IT move to GCP in 2018 (and beyond). DRA (Dynamical Risk Assessment) from ILFCRM (intelligence lead financial crime risk management) program, which is aim to catch the crime by new tech stack, including GCP (Dataflow/BigQuery/GKE, etc.) and Machine learning (SKlearn/Tensorflow/TFX and Kubeflow, etc.
- Design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions. Receiving models from data scientists, creating and using benchmarks and metrics, and making ML systems available/accessible to the systems that use/need it.
- Develop, deploy and maintain machine learning models, pipelines and workflows in production environment.
- Work with DevOps team to deploy and manage infrastructure for machine learning services
- Bachelor’s or Master’s degree in computer science, engineering or related field.
- 5+ years of experience in software development, machine learning engineering or related field.
- Programming languages like Shell, Python, SQL.
- Ability to understand tools used by data scientist (e.g. jupyter notebook, pandas, scipy) and experience with software development and test automation
- Docker, Kubernetes
- Strong understanding of machine learning concepts and frameworks, including TensorFlow, PyTorch, Scikitlearn, etc.
- Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, mlflow, Argo, etc.)
- Good English communication (verbal, written, email). Ability to explain complex ideas
- Ability to work in a team that is located across multiple countries/regions.
- Willingness to adapt and learn new things.
- Takes ownership of tasks.