Full time Machine Learning Engineer who is interested in developing cutting edge technologies for real-world next generation AI-driven insights platform. The ideal candidate will be equally comfortable with theoretical thinking, coding and academic writing.
- Implementing highly scalable Natural Language Processing (NLP) and Data Science services capable of processing extremely large volumes of data (in the order of several billions of documents per day).
- Contributing to the design of highly available, highly scalable, machine learning workflows.
- Create and implement non-standard, fresh and creative scientific ideas to improve or speed up existing technologies
- Push the ideas to improve the quality of designed models in terms of product requirements
- Support and justify the new ideas with a theoretical and intuitive basis
- Collaborate with product managers and dive deeper into the usage of the research, brainstorm
with the product team on developing new creative ideas
- Follow up and support the development of models in an end-to-end manner: from design to production
- Analyze, Interpret and explain outcomes of non-trivial experiments.
- Analyze the data used in experiments, understand the subtleties of it, modify appropriately if
- Communicate with data and product teams, write clear and understandable research requirement documents
- Collaborate with other scientists, exchange novel and interesting ideas, and reflect these ideas in the research process.
- Strong working knowledge (at least 5 years) of Java or Python.
- Strong working knowledge (at least 3 years) of machine learning frameworks, in particular TensorFlow and Keras.
- Good knowledge of data analysis and machine learning, in particular around Natural Language
- Good knowledge of micro-service design and implementation (e.g., SpringBoot, Gunicorn, Flask,
- Familiarity with infrastructure as code (e.g., Terraform, Cloud formation, Serverless), continuous integration and deployment (e.g., CircleCI, Drone.io, Jenkins), configuration management, dependency management (e.g., Maven, Gradle, PyPi), containerization technologies, e.g., Docker.
- Experience with performance-driven design.
- Good knowledge of Unix/Linux systems and bash/shell scripting.
UG : B.Tech/B.E. in Any Specialization
PG : Any Postgraduate