We are seeking an experienced Computer Vision MLOps Engineer to design, build, deploy, and manage production-grade computer vision and machine learning systems.

The role combines strong expertise in computer vision, machine learning engineering, cloud infrastructure, and MLOps practices to enable reliable and scalable deployment of vision-based AI solutions.

The ideal candidate will have experience taking computer vision models from development and experimentation through production deployment, monitoring, optimization, and continuous improvement.

Key Responsibilities :

- Design and develop scalable MLOps pipelines for computer vision and machine learning applications.

- Build and productionize computer vision models for image classification, object detection, image segmentation, OCR, tracking, and related use cases.

- Develop end-to-end workflows covering data preparation, model training, validation, deployment, monitoring, and retraining.

- Implement automated CI/CD pipelines for machine learning and computer vision model deployment.

- Containerize ML applications and models using Docker and deploy them across cloud or containerized environments.

- Design and manage model serving infrastructure for low-latency and high-throughput inference.

- Implement model versioning, experiment tracking, artifact management, and reproducible ML workflows.

- Build automated data and model pipelines to support continuous training and deployment.

- Establish monitoring mechanisms for model performance, data quality, latency, resource utilization, and production drift.

- Optimize computer vision inference pipelines for performance, scalability, and cost efficiency.

- Work on model optimization techniques such as quantization, pruning, batching, and hardware acceleration where required.

- Deploy and manage ML workloads across cloud platforms and Kubernetes-based environments.

- Collaborate with data scientists and computer vision engineers to convert experimental models into production-ready solutions.

- Develop APIs and services for integrating computer vision models with enterprise applications.

- Implement appropriate logging, alerting, observability, and failure-recovery mechanisms for production ML systems.

- Support troubleshooting of model, infrastructure, deployment, and inference-related production issues.

- Establish best practices around ML lifecycle management, governance, security, and deployment standards.

- Evaluate new MLOps tools, frameworks, and technologies to improve the overall ML development and deployment ecosystem.

- Mentor junior engineers and contribute to technical architecture and engineering standards.

Required Skills :

- 10 - 12 years of experience in Machine Learning, Computer Vision, MLOps, or related engineering roles.

- Strong hands-on experience with computer vision and deep learning applications.

- Experience with frameworks such as PyTorch, TensorFlow, OpenCV, or equivalent technologies.

- Strong programming skills in Python and experience developing production-grade ML applications.

- Hands-on experience building and managing end-to-end MLOps pipelines.

- Strong knowledge of ML lifecycle management, model deployment, model versioning, and monitoring.

- Experience with Docker, Kubernetes, and containerized ML workloads.

- Experience with CI/CD tools and automation for ML model deployment.

- Strong understanding of cloud platforms such as AWS, Azure, or GCP.

- Experience with ML orchestration and pipeline tools such as Kubeflow, MLflow, Airflow, or equivalent.

- Experience with REST APIs, microservices, and production model-serving architectures.

- Strong understanding of Git, Linux, automated testing, and deployment practices.

- Experience with model optimization and inference performance tuning.

- Good understanding of data pipelines, feature/data validation, model drift, and monitoring.

- Strong debugging and problem-solving capabilities.

- Experience designing scalable and highly available AI/ML platforms.

- Good communication skills with the ability to work across data science, engineering, product, and business teams