Responsibilities:
- Interact with clients and internal business teams to identify and define the right problems to solve
- Create state of the art, scalable, and self-learning solutions for real business problems. This involves:
- Choosing the most appropriate machine learning technique based on business needs and available data
- Training and tuning a variety of machine learning models which include traditional as well as deep learning models
- Analyzing and reporting model performance and accuracy
- Own the development and deployment of end-to-end machine learning solutions
- Data exploration and pre-processing
- Model training and optimization
- Model performance evaluation
- Solution deployment
- Monitoring solution outcome
- Rapidly prototype the solutions in an agile manner
- Experiment and develop novel machine learning algorithms in the area of smart document processing, image processing and speech analytics
- Deploy machine learning/ deep learning models in production: both on premise as well as cloud deployment
- Present advanced analytics solutions to clients and internal stake holders.
Requirements:
- 1-2 years of experience in developing and deploying machine learning solutions (including deep learning)
- Education: Bachelor's/Master's degree in quantitative fields (Statistics, Operations Research, Mathematics, Econometrics, Computer Science. Physics,
- Electrical Engineering etc.). An advanced degree is preferred.
- Organized, self-motivated, disciplined and detail oriented
- Experience with one or more of deep learning frameworks like Tensorflow, Torch, Keras, Caffe, MXNet Theano
- Ability to learn emerging Machine Learning and Deep Learning techniques and apply them to solve real-world problems
- Experience in the area of Computer Vision/Natural Language Processing
- Experience with model optimization on GPUs is preferred
- Knowledge of Dockers, Containers and model API development is a plus
- Experience with big data analytics technologies like Apache Spark, Kafka, Flume, ElasticSearch is a plus.