Applied Machine Learning / Computer Vision Engineer (Sahibzada Ajit Singh Nagar)

We are currently seeking an Applied Machine Learning / Computer Vision Engineer to join one of our clients innovative AI teams. This is an exciting opportunity to work on a next-generation AI platform combining Computer Vision, Multimodal AI, Applied Machine Learning, and Large Language Models . Key Responsibilities As an Applied ML / Computer Vision Engineer, you will: - Design, develop, and improve Computer Vision and image-processing pipelines. - Build solutions for video analysis, video understanding, and real-time visual processing. - Develop multimodal AI systems combining vision, audio, and language data. - Work with object detection, image segmentation, classification, tracking, and related Computer Vision techniques. - Implement and fine-tune Vision-Language Models and Transformer-based architectures. - Integrate Large Language Models into AI products for reasoning, analysis, and automated decision-making. - Evaluate, optimize, and deploy machine-learning models in production environments. - Improve model inference speed, accuracy, scalability, and resource efficiency. - Translate research papers and emerging AI techniques into practical product features. - Build APIs and production-grade services for AI model integration. - Collaborate with engineering and product teams to understand business requirements and deliver effective AI solutions. - Monitor model performance and continuously improve deployed systems. - Maintain explicit, reusable, and well-documented code. - 14 years of experienc e in Applied Machine Learning, Computer Vision, Deep Learning, or a related field. - Strong programming skills in Python. - Hands-on experience with PyTorch. - Practical knowledge of OpenCV and image or video processing. - Strong understanding of Deep Learning fundamentals. - Experience with object detection and/or image segmentation models. - Knowledge of Transformer architectures. - Experience working with Vision-Language Models. - Familiarity with the Hugging Face ecosystem. - Experience with model inference, evaluation, and optimization. - Understanding of how to move models from experimentation into production. - Experience using Docker and Git . - Ability to independently investigate technical challenges and propose practical solutions. - Ability to understand, reproduce, and implement methods described in research papers. - Strong analytical and problem-solving skills. - Curiosity and willingness to explore new technologies. - Passion for AI, Machine Learning, and product development. - Ownership mindset and accountability for delivered solutions. - Ability to work independently and contribute within a collaborative team. - Interest in building practical products rather than focusing only on model experimentation. - Comfortable working in a fast-moving and research-driven environment. Nice to Have Experience with one or more of the following technologies would be considered an advantage: - YOLO - GroundingDINO - SAM or SAM2 - Florence-2 - Whisper - Qwen2.5-VL - LLaVA - InternVL - CUDA - TensorRT - ONNX - FastAPI (Exceptional fresh graduates may also be considered if they can demonstrate strong technical knowledge through relevant academic work, personal projects, GitHub repositories, Kaggle participation, research, or a technical portfolio.) .