AI Engineer / Machine Learning Engineer – MLOps

Role Overview

We are looking for an AI Engineer with strong experience in Machine Learning Operations (MLOps) to design, deploy, monitor, and maintain machine learning and AI models in production environments.

What You Will Do

Build and maintain ML pipelines for training, testing, and deployment, deploy machine learning and AI models into production environments, manage model lifecycle, automate workflows using CI/CD for ML models, and monitor model performance, drift, and data quality.

Why It Might Be a Fit

Candidate should have experience in some of the following MLOps tools: MLflow, Kubeflow, Airflow, DVC, Weights & Biases, SageMaker, Azure ML, Vertex AI, Docker, Kubernetes, Terraform.

Requirements

• Python
• Machine Learning
• MLOps tools and frameworks
• Docker
• Kubernetes
• CI/CD (GitHub Actions, Jenkins, GitLab CI)
• MLflow / Kubeflow / Airflow
• Data pipelines
• APIs (FastAPI / Flask)
• Cloud platforms (AWS / Azure / GCP)
• SQL / NoSQL databases
• Model monitoring and logging
• MLOps Tools (Important)

Benefits

• Competitive compensation package
• Opportunities for professional development and career advancement
• Flexible working conditions, with remote options available
• Dynamic and supportive work environment