The AIWare.AI platform for Financial Inclusion aims to simplify banking services by making it accessible and intuitive for consumers yet efficient for bankers.
As the Machine Learning Lead, you will:
> Be part of our team of innovators, thinkers, dreamers and doers
> Build intelligence into our products to make them run smarter.
> Partner with Chief Architect to understand the problem to be solved, break down scope, set milestones and understand the technical solution
> Coordinate the efforts to understand the key customer pain points, business drivers, regulations, technological challenges, and lead the engineering team to eliminate these obstacles.
> Lead the implementation of the solution and work closely with Sales & GTM teams ensuring a shared vision for the future and drive the prioritization of the implementation roadmap based on customer inputs and market drivers.
What we care about
> Passionate in learning, researching, and creating products with high customer impact
> Share our values, of Innovation & Trust.
> Hands on experience in designing ML models and leading development of production grade ML projects.
> Prior domain knowledge in speech, natural language processing, or computer vision is strongly preferred
> Solid knowledge of fundamentals of statistics, machine learning, and deep learning is required
> Demonstrable track record of working with Product teams, prioritizing needs, and delivering results in a dynamic environment
> 3+ years of experience in design/implementation/ experience of Machine Learning/AI/Deep Learning solutions
> 1+ years of experience with one or more Deep Learning frameworks such as Apache MXNet, TensorFlow, Caffe2, Keras, Torch and Theano
> 8+ years professional experience in software development in languages related to ML like Python.
> Experience working with GPUs to develop models
> Experience handling large datasets
> Familiarity with using data visualization tools
> Experience working with RESTful API and general service-oriented architectures.
> Experience in building models using large scale data especially images using DL techniques like CNN/RNN
> Hands on experience in working with traditional ML problems like Classification/Regression/Anomaly Detection.
> Experience in NLP Packages (Any of Core NLP/Open NLP).
> Experience using ML libraries, such as scikit-learn
> Exposure to data engineering approaches leveraging Kafka, Spark and Big Data tools
Tagged as: AI, deep learning, developer, lead, machine learning, ML
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