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Job Description

Job Description

• Partners with data scientists, data managers, analysts, infrastructure engineers and peer AI Engineers to develop, operationalize, integrate, and scale new algorithmic products.
• Engages in proof of concepts and experiments to evaluate new models and technology.
• Develops high quality, standard-compliant and tested code. Refactors code of others as necessary.
• Contributes code and tools to central re-usable libraries and repositories.
• Develops data models, model features, data quality tests, ETL/ELT pipelines, and distributed compute architectures.
• Deploys algorithmic products across the clouds, skilfully leveraging cloud-native services. Manages and automates cloud resources (IaC).
• Implements DevOps, DataOps and MLOps principles in day-to-day work. Develops continuous integration, delivery, and training pipelines.
• Leverages observability and automation tools to manage operations of algorithmic products across the stack.

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Job Qualifications

Qualifications

• A Bachelors or Master’s degree in Computer Science, Software Engineering or related field
• 4+ years of proven experience in Python & Pyspark, data modelling, design, development, and deployment of Data and Analytics products
• Demonstrated engineering experience in operationalizing, integrating, and scaling up algorithmic products
• Experience in writing python production-level code and code patterns
• Experience in MLOps, DevOps, or software engineering, with a specialisation in machine learning
• Demonstrated experience in building and managing machine learning pipelines
• Strong background in containerization and orchestration technologies (Docker, Kubernetes)
• Experience in Agile, CI/CD and DEVOPS methodologies – knowledge of using appropriate tools – e.g. Jira, GitHub Actions, unit testing & mocking, static code analysis (e.g. Sonarqube), source control management tools (e.g. GitHub)
• GCP/Azure cloud computing knowledge and services related to machine learning and large-scale computing
• Understand best practices for data security, privacy and compliance in the context of AI
• Strong written and verbal communication in English

Preferred skills:

• Experience with AI / ML products and deployment of products to production (such as Azure Machine Learning and Databricks)
• Experience in monitoring and logging ML models
• Knowledge and experience in machine learning algorithms development
• Experience with source control management tools (GitHub)
• Desire to write testable code, and the test coverage to go along with it
• CI/CD tools knowledge and experience, familiar working in a DevOps environment
• Strong problem-solving skills paired with experience in programming, such as Python
• Ability to communicate technical concepts to teammates and non-technical colleagues

Nice to have:

• K8 and connected technologies (Helm, Kustomize.io, etc.) knowledge
• Infrastructure build tools and languages knowledge – Terraform, Ansible

Logging and monitoring technologies knowledge (depending on the needs, from Logstash to Grafana, Kibana, fluentd)

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