Machine Learning Operations Engineer
Flash 2023-04-12
Location: Cape Town CBD
Job Ref #: FH-198
Industry: Fintech
Job Type: Permanent
Positions Available: 1
We are looking for a Machine Learning Engineer to lead the designing and executing of the ML Ops strategy for the Data Science team. For this role, you should have significant experience in working with a variety of technologies related to Data Ops, ML Ops and AI. Critical thinking and problem-solving skills are essential for overcoming challenges to build and support a top-class platform.
Job Description
Responsibilities:
- Contribute to the Data Science and AI strategy, particularly the strategy for operationalizing machine learning models.
- Design and build automated, scalable and reliable Machine Learning CI/CD pipelines, including automatic re-training, re-testing and re-deploying.
- Manage and monitor productionized machine learning models.
- Enhance data collection procedures to include information that is relevant for building analytic systems.
- Assist in the integration and development of an external analytics system, which involves various data processing and data science technologies.
- Enable smarter processes and implement analytics for meaningful insights.
- Keep current with technical and industry developments.
- Communicate findings to all stakeholders.
Job Requirements
Minimum Requirements:
- B. Sc or B. Com in Physics, Computer Science, Applied Mathematics, Statistics, Data Science, Software Engineering or similar.
- 5+ years of relevant professional experience, preferably in the fintech industry.
Knowledge / Skills:
- Strong analytical and problem-solving skills.
- Expert in Python and SQL.
- Experience with the modern software development best practices, e.g.:
- Agile software development
- Code reviews
- Unit testing
- Version control, e.g. git
- CI/CD
- Experience with microservice architectures.
- Experience working in an agile team.
- Experience with ML frameworks and tools (e.g. pandas, numpy, scikit-learn, TensorFlow, Pytorch, Spark MLlib).
- Experience with cloud-based services such as Azure, AWS, etc.
- Experience with modern ETL, compute and orchestration frameworks, e.g. Apache Spark, Apache Flink, Apache Kafka, etc.
- Experience with container technologies, e.g. Docker, Kubernetes.
- Experience in building machine learning or AI systems.
- Experience deploying models to production.
- Experience working with ML platforms, e.g. MLflow, Kubeflow, etc.
- Experience with cloud-based infrastructure, e.g. Azure, AWS, GCP; ideally AWS.
- Experience with robotic automation of processes within the Financial Services industry.
Attributes:
- Self-motivated and assertive.
- Strong analytical ability.
- Strong verbal and written communication/ presentation skills.
- Team player and ability to operate independently.
- Good interpersonal skills.
- Trustworthy.
- Ability to prioritise.
- Ability to work in a pressured environment.
- Deadline driven.
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