MLOps Engineer

Job Description MLOps Engineer

Experience: 7+ Years
Location: Bellandur, Bangalore
Shift: 1:30 PM 10:30 PM

Work Mode: Monday - WFH and remaining( 4days - work from office)

Mandatory Skill: Azure Cloud Architecture

Key Skills

MLOps, Python, CI/CD, GitHub, Ansible, Azure DevOps, Azure Machine Learning, MLflow, Docker, Kubernetes, Computer Vision.

Role Overview

We are looking for an experienced MLOps Engineer with strong expertise in Python, Azure Cloud Architecture, and production-scale machine learning systems. The ideal candidate will be responsible for building, deploying, and maintaining scalable ML pipelines and automating the end-to-end machine learning lifecycle.

Key Responsibilities

1. Machine Learning & Python Development

• Develop solutions using Python, leveraging object-oriented and/or functional programming.
• Build, deploy, and support production-grade machine learning systems.
• Apply Computer Vision and Cognitive Services to enterprise AI solutions.
• Integrate ML models with unstructured data to support business use cases.

2. MLOps & ML Platform Engineering

• Design and maintain end-to-end MLOps pipelines covering model training, experiment tracking, versioning, deployment, monitoring, and automated retraining.
• Implement MLflow or equivalent tools for experiment tracking and model lifecycle management.
• Develop APIs for model training and inference, supporting batch and real-time processing.
• Automate ML workflows using CI/CD pipelines, GitHub, Azure DevOps, and Ansible.

3. Azure Cloud & Containerization

• Design and implement cloud-native ML solutions using Microsoft Azure.
• Build and deploy ML pipelines using Azure Machine Learning SDK.
• Work with Azure Machine Learning, Microsoft Fabric (where applicable), Docker, and Kubernetes.
• Manage scalable ML/DL workloads, including distributed training and parallel model execution.

4. Data Engineering & Performance Optimization

• Orchestrate large-scale machine learning and deep learning workloads using modern data engineering and container orchestration tools.
• Optimize model performance and perform hyperparameter tuning at scale.
• Work with data engineering technologies such as Azure Data Factory, Databricks, and Apache Spark.
• Apply knowledge of linear algebra, probability, statistics, and algorithms to machine learning problems.

5. Stakeholder Collaboration

• Collaborate with technical teams and business stakeholders to identify AI/ML opportunities.
• Develop solutions that improve business workflows and enable data-driven decision-making.
• Communicate complex technical concepts effectively to stakeholders at different organizational levels.

Required Skills

• Strong hands-on experience in MLOps and production-scale ML systems.
• Proficiency in Python programming.
• Mandatory experience in Azure Cloud Architecture.
• Experience with Azure Machine Learning, Azure DevOps, and CI/CD automation.
• Hands-on knowledge of GitHub, Docker, Kubernetes, and MLflow or equivalent tools.
• Exposure to Computer Vision, Cognitive Services, and unstructured data processing.
• Knowledge of Databricks, Apache Spark, and Azure Data Factory is preferred.