Location: Bellandur, Bengaluru

Experience: 7–8 Years

Employment Type: Permanent

Openings: 1

Shift: 1:30 PM – 10:30 PM IST

About The Role We are seeking an experienced MLOps Engineer with strong expertise in Python, CI/CD, GitHub, Ansible, Azure DevOps, and Azure Cloud Architecture . The role focuses on building, deploying, and maintaining production-scale machine learning systems, automating ML workflows, and developing scalable cloud-native AI solutions. The ideal candidate will have hands-on experience with Azure Machine Learning, MLflow, Docker, Kubernetes, and distributed ML workloads .

The MLOps Engineer will design, implement, and maintain end-to-end MLOps frameworks covering model training, experiment tracking, versioning, deployment, monitoring, and automated retraining. The candidate will develop custom APIs for model training and inference, supporting both batch and real-time workloads.

The role involves building cloud-first ML solutions using Microsoft Azure , including Azure Machine Learning, and deploying containerized workloads using Docker and Kubernetes . The engineer will leverage MLflow or equivalent tools for experiment tracking and lifecycle management while optimizing machine learning and deep learning workloads for performance and scalability.

The candidate will also work with data engineering technologies such as Databricks, Apache Spark, and Azure Data Factory to integrate machine learning models into enterprise AI workflows. Collaboration with technical teams and business stakeholders is essential to identify opportunities for machine learning and analytics to improve workflows and support data-driven decision-making.





Key Responsibilities

- Design, implement, and maintain end-to-end MLOps frameworks for model training, experiment tracking, versioning, deployment, monitoring, and retraining.
- Build and support production-scale machine learning systems using Python and object-oriented or functional programming principles.
- Implement MLflow or equivalent frameworks for experiment tracking, model lifecycle management, and reproducibility.
- Develop custom APIs for ML model training and inference across batch and real-time workloads.
- Build and deploy ML pipelines using Azure Machine Learning SDK and other relevant Azure services.
- Design and manage containerized ML workloads using Docker and Kubernetes.
- Orchestrate distributed training and large-scale ML/DL workloads on cloud infrastructure.
- Apply model optimization and hyperparameter tuning techniques to improve model performance.
- Integrate ML models with unstructured data and enterprise AI workflows, including computer vision and cognitive services where applicable.
- Work with Databricks, Apache Spark, and Azure Data Factory to support data processing and ML workflows.
- Collaborate with technical experts and business stakeholders to identify and implement ML-driven business improvements.




- Communicate complex technical concepts clearly through documentation and stakeholder discussions.

Required Skills

- MLOps and ML Platform Engineering
- Python Programming – Object-oriented and/or functional programming.
- CI/CD and GitHub
- Ansible and Azure DevOps
- Microsoft Azure Cloud Services
- Azure Machine Learning and Azure Machine Learning SDK
- MLflow or equivalent experiment tracking and model lifecycle tools.
- Docker and Kubernetes
- Machine Learning and Deep Learning model deployment and optimization.
- Custom API Development for model training and inference.
- Distributed Training and Large-Scale ML Workloads
- Computer Vision and Cognitive Services exposure.
- Databricks, Apache Spark, and Azure Data Factory
- Understanding of linear algebra, probability, statistics, and algorithms.
- Strong analytical, problem-solving, and communication skills.

Mandatory Skills
- Azure Cloud Architecture

Candidate Profile The ideal candidate will have hands-on experience operationalizing machine learning models and supporting production-scale AI systems in cloud environments. They should be capable of designing scalable ML platforms, automating deployment and monitoring workflows, and optimizing distributed ML workloads. Strong collaboration, analytical thinking, and communication skills are essential.

Benefits Package

- Competitive salary and benefits package.
- Opportunities for professional growth and development.
- A collaborative and inclusive work environment.
- The prospect to work on exciting and challenging projects with leading clients.

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