Use machine learning & deep learning techniques to create new, scalable solutions for business problems.
Develop NLP, NLU, NLG, NER, computer vision models and technologies for acquiring, parsing, interpreting and visualizing structured and unstructured data
Running regular benchmarking tests and perform statistical analysis, draw conclusions on the impact of your research-based optimizations to provide thought leadership to the team
Analyze and extract relevant information from large amounts data to help in automating the workflows and optimizing key processes.
Help the team in building large scale online learning system.
Help the team to build research to production pipeline.
Stay current with the latest research and technology and communicate your knowledge throughout the enterprise
Come up with patentable ideas to provide us competitive advantage.
What you will bring
Post Graduate / Graduate in computer science or a related field and a strong math background.
Overall 8+ years of experience in IT industry with 2-4 years working on Machine Learning & Statistics projects.
Experience working with Machine Learning pipelines - data ingestion, feature engineering, modeling, predicting, explaining, deploying and monitoring ML models.
Strong knowledge and 3-5 years of hands on experience with Java, Python, R, C / C++ or similar scripting languages and general software development skills (source code management, debugging, testing, deployment, etc.)
Experience with one or more open-source toolkits such as CoreNLP, OpenNLP, NLTK, OpenCV etc.
Experience with one or more Deep Learning frameworks like TensorFlow, PyTorch, CNTK, Caffe, Keras, DeepLearning4J etc.
Experience with GIT, REST APIs, containerization/container management.
Experience with Azure, GCP and/or AWS.
PhD in Computer science or related field with focus on Deep Learning.
Experience building Neural Networks for object detection/recognition, image classification, image segmentation, handwriting recognition.
Experience with continuous learning & transfer learning.
Experience with big data frameworks like Cloudera, Spark, Bigquery, & Kafka.
Familiarity with large data sets, cloud-based development and deployment, open source practices and frameworks and experience in putting AI applications in production.
Publications in top conferences such as NIPS, CVPR, ICLR, ICML