Job
Description
Looking for a skilled Senior Data Science Engineer with 6-12 years of experience to lead the development of advanced computer vision models and systems. The ideal candidate will have hands-on experience with state-of-the-art architectures and a deep understanding of the complete ML lifecycle. This position is based in Bengaluru. Roles and Responsibility Lead the development and implementation of computer vision models for tasks such as object detection, tracking, image retrieval, and scene understanding. Design and execute end-to-end pipelines for data preparation, model training, evaluation, and deployment. Perform fine-tuning and transfer learning on large-scale vision-language models to meet application-specific needs. Optimize deep learning models for edge inference (NVIDIA Jetson, TensorRT, OpenVINO) and real-time performance. Develop scalable and maintainable ML pipelines using tools such as MLflow, DVC, and Kubeflow. Automate experimentation and deployment processes using CI/CD workflows. Collaborate cross-functionally with MLOps, backend, and product teams to align technical efforts with business needs. Monitor, debug, and enhance model performance in production environments. Stay up-to-date with the latest trends in CV/AI research and rapidly prototype new ideas for real-world use. Job Requirements 6-7+ years of hands-on experience in data science and machine learning, with at least 4 years focused on computer vision. Strong experience with deep learning frameworks: PyTorch (preferred), TensorFlow, Hugging Face Transformers. In-depth understanding and practical experience with Class-incremental learning and lifelong learning systems. Proficient in Python, including data processing libraries like NumPy, Pandas, and OpenCV. Strong command of version control and reproducibility tools (e.g., MLflow, DVC, Weights & Biases). Experience with training and optimizing models for GPU inference and edge deployment (Jetson, Coral, etc.). Familiarity with ONNX, TensorRT, and model quantization/conversion techniques. Demonstrated ability to analyze and work with large-scale visual datasets in real-time or near-real-time systems. Experience working in fast-paced startup environments with ownership of production AI systems. Exposure to cloud platforms such as AWS (SageMaker, Lambda), GCP, or Azure for ML workflows. Experience with video analytics, real-time inference, and event-based vision systems. Familiarity with monitoring tools for ML systems (e.g., Prometheus, Grafana, Sentry). Prior work in domains such as retail analytics, healthcare, or surveillance/IoT-based CV applications. Contributions to open-source computer vision libraries or publications in top AI/ML conferences (e.g., CVPR, NeurIPS, ICCV). Comfortable mentoring junior engineers and collaborating with cross-functional stakeholders.
