Vertex AI / MLOps Engineer

Role Summary

We are seeking a skilled
Vertex AI / MLOps Engineer
to design, build, and manage scalable machine learning pipelines on
Google Cloud Platform (GCP)
. The ideal candidate will have strong experience in
Vertex AI, traditional machine learning, and supervised learning models
, along with hands-on expertise in deploying and operationalizing ML solutions using MLOps best practices.

Key Responsibilities

• Design, develop, and deploy end-to-end ML pipelines using Vertex AI .
• Build, train, evaluate, and optimize traditional supervised learning models (regression, classification, etc.).
• Implement MLOps practices including model versioning, monitoring, CI/CD, and automated retraining.
• Manage model lifecycle: data ingestion, feature engineering, training, deployment, and performance tracking.
• Develop scalable and reusable workflows using Vertex AI Pipelines .
• Deploy models using Vertex AI Endpoints for real-time and batch predictions.
• Monitor model performance, drift, and data quality in production environments.
• Collaborate with data engineers, cloud architects, and business stakeholders to translate requirements into ML solutions.
• Optimize cloud resources for cost, performance, and scalability.
• Maintain documentation, governance, and compliance for ML deployments.

Required Skills & Qualifications

• 5+ years of experience in Machine Learning / MLOps .
• Strong hands-on experience with Google Cloud Platform (GCP) .
• Expertise in Vertex AI (training, pipelines, model registry, deployment, monitoring).
• Solid understanding of traditional machine learning algorithms and supervised learning techniques .
• Proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch .
• Experience with CI/CD tools , containerization ( Docker ), and orchestration.
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques .
• Knowledge of REST APIs and model serving frameworks