About Us :

At Hoomanely, we're building a precision health platform for pets - a sensor-driven, AI-powered ecosystem that transforms everyday health signals into actionable insights.

Our products combine hardware, software, and machine learning to help pet parents better understand and monitor their pets' health.

About the Role :

We're looking for a hands-on Machine Learning Engineer with strong experience in Deep Learning and Computer Vision to develop, train, evaluate, deploy, and maintain ML models.

The ideal candidate should have practical experience in image classification, object detection, image segmentation, and CNN based architectures, along with exposure to time-series analysis, audio processing, and sensor fusion.

What You'll Do :

- Develop and train deep learning and computer vision models for image classification, object detection, and image segmentation.

- Work with CNN architectures such as YOLO, ResNet, EfficientNet, MobileNet, and U-Net.

- Prepare, label, preprocess, and augment custom datasets.

- Use Python, PyTorch, TensorFlow/Keras, and OpenCV for model development.

- Perform model training, validation, fine-tuning, hyperparameter tuning, and performance evaluation.

- Work with time-series data, audio signal processing, and sensor fusion.

- Deploy and optimise models for production, edge, and embedded environments.

- Work with ONNX, TensorFlow Lite, and model optimisation techniques.

- Monitor model performance, detect data/model drift, and support retraining pipelines.

- Collaborate with hardware, firmware, and application teams.

Mandatory Skills & Experience :

- B.Tech from a Tier 1 institute (IIT, NIT, BITS Pilani or equivalent).

- 3+ years of applied Machine Learning and Deep Learning experience.

- Strong hands-on experience in Computer Vision.

- Practical experience in image classification, object detection, and/or image segmentation.

- Experience training and fine-tuning deep learning models on custom datasets.

- Strong understanding of CNN architectures such as YOLO, ResNet, MobileNet, and EfficientNet.

- Proficiency in Python, PyTorch or TensorFlow/Keras, and OpenCV.

- Knowledge of model evaluation metrics such as accuracy, precision, recall, F1-score, mAP, and IoU.

- Understanding of time-series analysis and CNNs, RNNs, or signal processing.

- Experience taking ML models from prototype to production deployment.

Good to Have :

- Experience with ONNX, TFLite, and Edge AI.

- Knowledge of model quantisation, pruning, and inference optimisation.

- Experience with audio processing and accelerometer-based activity classification.

- Exposure to MLOps, model monitoring, and retraining pipelines.

- Experience in IoT, wearables, or health-tech.

Why Join Us?

- Work on real-world Deep Learning and Computer Vision applications.

- Build models deployed on actual hardware.

- Work closely with the ML Lead and take ownership of key projects.

- Join a fast-moving team building innovative pet health technology.