Machine Learning Engineer

Role Overview

As an MLOps Engineer, you will build, deploy, and maintain scalable machine learning systems and pipelines. You’ll work closely with data scientists, software engineers, and stakeholders to ensure ML models integrate reliably into production.

Responsibilities

• Design, develop, and maintain scalable ML pipelines and workflows
• Deploy and monitor machine learning models in production
• Collaborate cross-functionally to operationalize ML solutions
• Ensure reliability, scalability, and performance of ML systems
• Apply best practices for model versioning, monitoring, and governance
• Optimize infrastructure and workflows for cost and efficiency

Required Qualifications

• 3–5 years of experience in MLOps, Machine Learning Engineering, or related field
• Strong proficiency in Python for data/ML engineering pipelines
• Hands-on experience with Google Cloud Platform (GCP)
• Experience with Vertex AI and Cloud Build
• Strong knowledge of BigQuery SQL
• Experience with Docker and containerization
• Solid understanding of the ML lifecycle (Training to Serving)
• Experience maintaining and deploying model endpoints using appropriate frameworks

Preferred Qualifications

• Experience with FastAPI or similar frameworks
• Knowledge of model deployment strategies (batch and real-time)
• Familiarity with Kubernetes and release management