Job Location: Bangalore/Bengaluru
Job Functions:
– Expertise in DNN Architectures {CNNs, LSTMs, Transformers} applied to Speech and Language Problems such as Question Answering, Summarisation, Semantic Understanding
– Review published literature, conceptualize novel algorithms, implement, evaluate and facilitate deployment of solutions for speech, natural language and dialog problems encountered in human conversations and analytics.
– Develop rich models for specific tasks by collecting, curating, coordinating annotations of spoken conversations, training and adapting machine learning models
– Tune model performance through feature engineering to optimize model performance by combining rules and machine learning techniques
– Integrate the developed models into Curo software and deploy them on Interactions Platforms.
– Document work through conference publications, file patent disclosures.
– Mentor junior associates as required.
Experience and Skills required:
– Masters (With 4 years of experience) or PhD degree (Fresher of With 1 years of experience) in Computer Science/ Statistics with experience in Machine Learning
– Proven success in applying Machine Learning models to practical problems
– Understanding of word & sentence representations like Word2Vec, Glove, Bert, ELMO etc
– Good understanding of pattern recognition algorithms like k-means, SVM, HMM, GMM, Neural Networks, Viterbi decoding etc
– Expertise in Python/C/C++
– Experience contributing to research efforts, including publishing in conferences
– Ability to demonstrate Interactions Values of:
– Being passionate about customer service
– Obsessing with our customer’s success
– Respecting each other
– Creating opportunity
– Embracing disruption
– Doing what we say we will do
Pluses:
– Experience working with machine learning tools, DNN tools, speech recognition tools, web crawlers, finite state machines, and open source natural language toolkits are a plus.
– Experience working with deep learning toolkits like PyTorch, Tensorflow etc.
Experience in natural language processing technologies and services with emphasis in one or more of the following:
– Data acquisition and NL modeling: harvesting NL resources from the Web, rapid bootstrapping of domain-specific and multilingual NL models for named-entity, syntactic parsing and text classification
– NL systems: large-scale development and deployment, performance monitoring, tuning and optimization of NL models
– NL methodology: grammar-based, data-driven and machine learning-based, hybrid approaches
– NL technologies: spoken language understanding, language translation, natural language search, syntax-semantics.
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