Responsibilities :

1. Technically and managerially lead teams on multiple initiatives

2. Design and implement novel approaches to deep learning from large scale data using ML models and implement visualization layers for rich user experience

3. Create detailed requirements and define problem statement (input/output)

4. Create solution architecture in collaboration with domain experts, data engineers and User experience developers

5. Build Deep Learning model and/or select right pre-trained model to achieve desired outputs

6. Work with Data Engineers to pre-process acquired data including sensors data, images, videos

7. Identify right tools and libraries to build solution

8. Build PoC (Proof of Concept) to demonstrate concept and for seeking feedback from stake holders including prospective customers and domain experts

9. Work with Engineers and Developers to productize model to build domain specific solutions

10. Presentation to multiple stakeholders on the project progress and related commercials.

Experience (Must have) :

1. 8+ years of Software Development, including 4+ years as a Machine Learning/Deep Learning Engineer building and deploying AI ML solutions

2. Good understanding of machine learning concepts: regression, classification, clustering

3. Proficient at summarising and visualising complex data and pattern finding

4. Indepth knowledge of Image processing and computer vision

5. Experience with development of customized Deep Learning networks like CNN/RNN

6. Good knowledge of classification, segmentation and clustering techniques

7. Familiar with deep learning principles like deep neural nets, conv nets, recurrent nets, Unet, Enet

8. Well vesed with NLP and techniques like Knowledge Graph to build up contextual engines using unstructured data

9. Familiar with Re-enforcement learning

10. Programming languages - Python (must have), C/C++, Java, R

11. Computer Vision, OpenCV and/or Open VINO

12. Experience with projects of visual object detection/object recognition, image segmentation, image classification, visual search.

13. Libraries: Tensorflow, Keras, Theano, Torch, Caffe, numpy, pandas, scikit learn

14. Experience in applying statistical approaches while development software solutions for data analysis

15. Various tools and techniques to optimize the model, create smaller footprint for embedding, closely tying up with processor (edge computing). Relevant embedded experience to make system run on target hardware platform

16. Excellent communication skills and an ability to present complex ideas

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