Description : MLOps Engineer (Azure)

Mumbai / Bengaluru / Remote

Es Magico

About Es Magico :

Es Magico is an AI-first venture studio and enterprise technology partner building production-grade AI platforms, agentic AI systems, and scalable data products for enterprises, startups, and government organizations across India, the US, and Southeast Asia.

We are Azure-first and work on real-world, high-impact AI systems at scale.

Role Overview :

We are looking for an MLOps Engineer (Azure) to help design, deploy, and operate enterprise-scale machine learning systems.

This role focuses on taking ML models from experimentation to secure, reliable, and scalable production environments, following best practices seen in large global organizations.

You will work closely with Data Science, Backend, DevOps, and Product teams to ensure ML solutions are production-ready and business-critical.

Key Responsibilities :

- Build and maintain end-to-end MLOps pipelines on Azure for training, validation, deployment, and monitoring.

- Design and operate ML infrastructure using Azure Machine Learning, AKS, and containerized workloads.

- Implement CI/CD pipelines for ML using Azure DevOps or GitHub Actions.

- Manage the full model lifecycle including versioning, promotion, rollback, and retraining.

- Support batch and real-time inference at scale.

- Implement monitoring and observability for model performance, data drift, and system health.

- Ensure security, access control, and compliance of ML systems in production.

- Collaborate with Data Scientists to productionize models and with DevOps teams on cloud standards.

- Contribute to defining MLOps best practices and platform standards.

Required Skills & Experience :

- 3+ years of total experience in software engineering, ML engineering, or MLOps.

- Hands-on experience with Microsoft Azure, including :

- Azure Machine Learning

- Azure Kubernetes Service (AKS)

- Azure Blob Storage / Data Lake

- Azure Container Registry

- Experience with Docker and Kubernetes.

- Strong Python skills with experience deploying ML models.

- Experience building or maintaining CI/CD pipelines.

- Familiarity with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.

- Understanding of production monitoring, logging, and reliability.

Good to Have :

- Experience with LLMs, RAG pipelines, or GenAI systems.

- Familiarity with Infrastructure as Code (Terraform / Bicep).

- Exposure to enterprise or regulated environments.

- Experience supporting multi-team or multi-product ML systems.