Machine Learning Operations Engineer

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. 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 prioritize
• Ability to work in a pressured environment