We are looking for an experienced MLOps Engineer with 7+ years of experience in Python development, machine learning operations, cloud platforms, and production-scale ML systems. The ideal candidate will have hands-on experience designing and maintaining end-to-end MLOps pipelines, deploying machine learning models, and managing scalable ML workloads on Microsoft Azure.

Experience with CI/CD, GitHub, Azure DevOps, Ansible, Docker, Kubernetes, and ML lifecycle management is essential.

Key Responsibilities

- Design, implement, and maintain end-to-end MLOps frameworks for model training, experiment tracking, versioning, deployment, monitoring, and automated retraining.
- Build and support production-scale machine learning systems.
- Implement MLflow or equivalent tools for experiment tracking, model versioning, and lifecycle management.
- Develop APIs for model training and inference, supporting batch and real-time workloads.
- Build and deploy ML pipelines using the Azure Machine Learning SDK.
- Develop cloud-based ML solutions using Microsoft Azure and Azure Machine Learning.
- Manage containerized ML workloads using Docker and Kubernetes.
- Support distributed training and large-scale ML/DL workloads on cloud infrastructure.
- Work with computer vision, cognitive services, and unstructured data in enterprise AI workflows.
- Optimize models and perform hyperparameter tuning to improve performance and scalability.
- Collaborate with data scientists, ML engineers, data engineers, DevOps teams, and business stakeholders.
- Identify opportunities to apply machine learning and analytics to business problems.
- Communicate technical concepts clearly to technical and non-technical stakeholders.

Required Skills and Qualifications
- 7+ years of relevant professional experience.
- Solid hands-on Python programming experience,



including object-oriented and/or functional programming.
- Proven experience delivering and supporting production-scale ML systems.
- Practical experience with MLOps and end-to-end ML lifecycle management.
- Experience with CI/CD, GitHub, and Azure DevOps.
- Hands-on experience with Microsoft Azure Machine Learning and the Azure ML SDK.
- Experience with MLflow or an equivalent experiment-tracking and model lifecycle framework.
- Knowledge of Docker, Kubernetes, and containerized ML deployments.
- Experience with Ansible for automation and configuration management.
- Experience building and maintaining ML pipelines and model deployment workflows.
- Exposure to computer vision and/or cognitive services in production AI solutions.
- Experience integrating ML models with unstructured data in enterprise workflows.
- Understanding of model monitoring, optimization, reproducibility, and automated retraining.

Cloud, Data, and ML Platform Skills Candidates should have experience or working knowledge of the following:
- Cloud and ML: Microsoft Azure, Azure Machine Learning, Azure ML SDK, and Azure Fabric where applicable.
- MLOps: MLflow, experiment tracking, model versioning, model registry, deployment, and monitoring.
- DevOps: CI/CD, GitHub, Azure DevOps, and Ansible.
- Containers: Docker and Kubernetes.
- Data Engineering: Databricks, Apache Spark, and Azure Data Factory.
- Machine Learning: Model optimization, hyperparameter tuning, distributed training, and large-scale ML/DL workloads.
- Programming and Mathematics: Python, linear algebra, probability, statistics, and algorithms.

Pay: ₹600,000.00 - ₹2,400,000.00 per year

Application Question(s)

- By applying, you agree that your resume and personal details may be stored securely for up to 2 years for recruitment purposes.

Work Location: Hybrid remote in Bengaluru, Karnataka

📌 MLOps Engineer (Bengaluru)
🏢 Ananta Services
📍 Bengaluru