Machine Learning Engineer, Mid-level

Role Overview

• Support Data Scientists in developing Machine Learning models throughout the different stages of the model lifecycle
• Develop SQL queries and Python programs for data extraction and preprocessing
• Develop functions and notebooks using SQL and Python, applying programming logic and object-oriented programming concepts
• Develop, maintain and improve model deployment pipelines
• Ensure integration, security and integrity of the data used for feature development
• Develop robust, scalable solutions that run models correctly and are aligned with corporate architecture
• Execute unit tests on developed data products before production deployment
• Document data projects you work on clearly and concisely

Requirements

• Intermediate SQL (DQL, DML and DDL commands)
• Data capture from files, APIs and websites (web scraping)
• Knowledge of ETL
• Git (push, pull, branches, merge, pull requests, conflict resolution, code review)
• Intermediate Python programming logic: object-oriented programming, library/package creation, decorators
• Spark
• Testing methodologies and techniques
• Containers (Docker)
• Databricks
• Airflow
• Excel (mathematical functions, chart creation and pivot tables)
• Kafka / Event Hub
• Relational databases (e.g., SQL Server)
• NoSQL databases (e.g., MongoDB)
• Azure DevOps
• MLflow
• Intermediate architectural styles (data warehouse, data mart, Data Lake and Lakehouse)
• Cloud computing: Azure

Tech Stack

• Airflow
• Azure
• Cloud
• Docker
• ETL
• Kafka
• MongoDB
• NoSQL
• Python
• Spark
• SQL

Benefits

• Flexible working hours
• Collaborative work environment
• Professional development opportunities