Machine Learning Engineer (Computer Vision)

Overview

Build, deploy and operate AI and computer vision systems on Microsoft Azure, owning models end to end from data pipelines and training through production deployment, monitoring and retraining.

What you'll do

• Design, train and fine-tune models for image classification, object detection, segmentation and OCR.
• Build image and video processing pipelines with OpenCV, including augmentation workflows.
• Explore and integrate generative AI where it fits, such as Azure OpenAI and vision-language models.
• Build batch and streaming pipelines with Azure Data Factory, Azure Databricks and PySpark.
• Manage data in Azure Data Lake Storage and Blob Storage, including large image and video datasets.
• Ensure data quality, versioning and lineage for training and evaluation datasets.
• Run experiments, model registry and deployments in Azure Machine Learning (pipelines, managed online and batch endpoints).
• Build CI/CD for models and pipelines with Azure DevOps or GitHub Actions.
• Containerize services with Docker and deploy to Azure Kubernetes Service (AKS) or Azure Container Apps.
• Monitor models in production for drift, accuracy and latency with Azure Monitor and Application Insights, and automate retraining.
• Manage infrastructure as code with Terraform.
• Work with data scientists, product owners and software engineers to turn business problems into ML solutions.
• Write clear documentation and take part in code reviews.

What you'll need

• Bachelor's or Master's degree in Computer Science, Data Science, AI, Electrical Engineering or a related field.
• 3+ years of professional experience in machine learning engineering, with at least 2 years on computer vision projects.
• At least one computer vision model taken from prototype to production and maintained there.
• 2+ years working with Microsoft Azure data and ML services.
• Strong Python and solid software engineering habits: testing, version control, clean code.
• Good grasp of ML fundamentals: model evaluation, feature engineering, and deep learning architectures.
• Clear communication with both technical and non-technical stakeholders.

Nice to have

• Microsoft certifications: Azure Data Scientist Associate (DP-100).
• Experience with AgenticAI, RAG.

Details

• Location: Bangalore / Hybrid.
• Employment type: Full-time.
• Experience: 3 to 5 years.