Machine Learning Operations Engineer

Machine Learning Operations Engineer

Flash Date: 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:

1. Contribute to the Data Science and AI strategy, particularly the strategy for operationalizing machine learning models.
2. Design and build automated, scalable and reliable Machine Learning CI/CD pipelines, including automatic re-training, re-testing and re-deploying.
3. Manage and monitor productionized machine learning models.
4. Enhance data collection procedures to include information that is relevant for building analytic systems.
5. Assist in the integration and development of an external analytics system, which involves various data processing and data science technologies.
6. Enable smarter processes and implement analytics for meaningful insights.
7. Keep current with technical and industry developments.
8. Communicate findings to all stakeholders.

Job Requirements

Minimum Requirements:

1. B. Sc or B. Com in Physics, Computer Science, Applied Mathematics, Statistics, Data Science, Software Engineering or similar.
2. 5+ years of relevant professional experience, preferably in the fintech industry.

Knowledge / Skills:

1. Strong analytical and problem-solving skills.
2. Expert in Python and SQL.
3. Experience with 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:

1. Self-motivated and assertive.
2. Strong analytical ability.
3. Strong verbal and written communication/presentation skills.
4. Team player and ability to operate independently.
5. Good interpersonal skills.
6. Trustworthy.
7. Ability to prioritise.
8. Ability to work in a pressured environment.
9. Deadline driven.

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