Machine Learning Engineer

Role Overview

We are seeking a Machine Learning Engineer with 3–5 years of experience deploying and validating machine learning models in production environments, particularly on the Azure platform. This role is not responsible for developing or enhancing models. Instead, it focuses on collaborating with internal teams to deploy, validate, and productionize models developed by data scientists across multiple markets. The ideal candidate will have strong Python programming skills, a background in ML deployment pipelines, and the ability to work independently within a cross-functional team.

Key Responsibilities

• Deploy machine learning models developed by data scientists into production environments
• Validate model performance and ensure accuracy against production data
• Collaborate with data engineers to integrate models with data pipelines
• Work with DevOps engineers to embed model outputs into existing or new applications
• Use Azure-based tools and services (e.g., Azure Functions, Kubernetes) for scalable deployment
• Leverage Python libraries such as Pandas and Polars for data preparation and validation
• Implement and maintain automated testing frameworks to ensure model integrity before deployment
• Participate in the productionization of models across multiple markets and business units
• Follow best practices for model versioning, monitoring, and operational stability
• Support CI/CD workflows related to machine learning operations

Preferred Qualifications

• 3–5 years of experience in machine learning engineering focused on model deployment and validation
• Proficiency in Python, with experience using Pandas, Polars, and other data libraries
• Hands-on experience with Azure cloud services, Docker, and Kubernetes
• Exposure to ML-Ops principles, including model monitoring, retraining, and automation
• Familiarity with APIs and microservices used for serving machine learning outputs

Soft Skills

• Self-directed and comfortable working independently with minimal supervision
• Able to navigate ambiguity and proactively seek clarifications
• Strong internal communication and documentation skills
• Role is internally focused—no direct interaction with clients or external stakeholders is required