ML Ops Engineer

Must have skills required :

Class Incremental Learning, Computer Vision, Docker/Kubernetes, MLOps, Pytorch, TensorFlow, Vision Transformers, Machine Learning

Good to have skills :

edge deployment, MLFlow, ONNX, Prometheus/Grafana, Cloud Server (Google / AWS)

Radius AI (One of Uplers Clients) is Looking for:

ML Ops Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you.

Role Overview Description

Were looking for an experienced ML Ops Engineer with a strong foundation in computer vision and a passion for deploying production-grade AI systems. You'll lead the end-to-end lifecycle of our computer vision models from development and training to scalable deployment. The ideal candidate is excited to optimize and scale AI solutions in the real world.

Key Responsibilities

• Drive the end-to-end development and deployment of computer vision models. Build and maintain robust, scalable ML pipelines and deployment workflows. Implement cutting-edge techniques like Vision Transformers (ViT) and class-incremental learning.
• Collaborate closely with research scientists, data engineers, and software developers. Train Models and optimise them to run on edge GPUs.
• Automate training, validation, testing, and deployment using CI/CD tools. Monitor model performance in production and optimize for latency, accuracy, and scalability.
• Ensure reproducibility and versioning of datasets, models, and experiments.

Required Qualifications

• 4+ years of hands-on experience in machine learning and MLOps. Proven experience deploying computer vision models in real-world production environments.
• Proficiency in deep learning frameworks such as PyTorch or TensorFlow. Strong understanding of Vision Transformers (ViT), class-incremental learning, and model version control best practices.
• Experience with containerization tools like Docker and orchestration using Kubernetes.
• Familiarity with building and managing ML pipelines using tools such as MLflow, Experience automating CI/CD workflows for machine learning projects. Proficient in Python and scripting for automation.
• Understanding of data versioning tools and reproducibility standards (e.g., DVC, Weights & Biases).
• Strong problem-solving skills with the ability to debug and optimize deep learning models in production.
• Comfortable working with large-scale datasets and real-time data streaming environments.

Preferred Qualifications

• Experience working in startups or fast-paced product teams with a bias for action. Exposure to cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure for ML infrastructure and services.
• Familiarity with edge deployment strategies and tools like NVIDIA TensorRT or OpenVINO.
• Understanding of ONNX or other model conversion frameworks.
• Experience with real-time analytics systems and low-latency model serving. Familiarity with monitoring tools for production ML systems (e.g., Prometheus, Grafana, Sentry).
• Prior experience in retail or video analytics domains is a plus.
• Contributions to open-source ML/CV projects or research publications.