Senior AI Engineer

Eigenstate AI is looking for a Senior AI Engineer with strong hands-on experience in computer vision and deep learning.

The role involves designing, developing, optimizing and deploying AI solutions for real-world applications. We are looking for someone who can take ownership of end-to-end AI development, from problem-solving and model development to evaluation, optimization and production deployment.

• Design, develop and deploy computer vision systems for image and video-based applications.
• Build solutions involving object detection, classification, segmentation, tracking and visual understanding.
• Develop and optimise models using architectures such as CNNs (ResNet, EfficientNet), Vision Transformers (ViT, Swin, DeiT) and detection/segmentation models (YOLO, DETR, Mask R-CNN).
• Implement multi-object tracking algorithms such as SORT, DeepSORT and ByteTrack.
• Develop video understanding and temporal reasoning capabilities using RNNs, Transformers and other relevant approaches.
• Explore multimodal AI systems combining vision and language using models such as CLIP, BLIP and similar architectures.
• Apply classical computer vision and geometric techniques, including camera calibration, pose estimation, feature matching and depth estimation.
• Train, fine-tune, evaluate and optimise deep learning models for accuracy, latency and scalability.
• Build data pipelines for dataset curation, annotation, augmentation and model training.
• Deploy and integrate AI models into production environments, including GPU-based and edge infrastructure.
• Research and experiment with emerging AI techniques to solve complex technical challenges.

• 4+ years of hands-on experience in computer vision and applied deep learning.
• Strong programming skills in Python.
• Proficiency in PyTorch or TensorFlow.
• Practical experience with object detection, segmentation and tracking systems.
• Experience with model training, fine-tuning, evaluation and optimisation.
• Strong understanding of representation learning, loss functions and deep learning fundamentals.
• Good understanding of evaluation metrics such as mAP, IoU, precision, recall and F1-score.
• Experience with image and video processing using OpenCV or similar frameworks.
• Experience building and deploying end-to-end AI solutions in production environments.
• Understanding of inference optimisation, GPU acceleration and scalable deployment.

• Experience with multimodal models such as CLIP, BLIP or similar Vision-Language Models.
• Knowledge of action recognition, video understanding and temporal modelling.
• Experience with human pose estimation, skeletal tracking and 3D vision.
• Familiarity with NeRFs, SLAM, point clouds and depth estimation.
• Experience with ONNX, TensorRT, CUDA and model quantisation.
• Knowledge of active learning, hard-negative mining and synthetic data generation.
• Experience with distributed training and large-scale data pipelines.
• Familiarity with Docker, cloud infrastructure and edge deployment.

• Strong practical experience building and deploying AI solutions, beyond academic projects and experimentation.
• Ability to independently approach complex computer vision problems and identify suitable technical solutions.
• Strong understanding of the complete model development lifecycle.
• Ability to translate research and experimentation into reliable, production-ready systems.
• Strong ownership, problem-solving skills and collaboration with cross-functional engineering teams.

Candidates whose experience is limited to academic projects or model experimentation without practical implementation and deployment experience may not be suitable for this role.