Tech Stack
AWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesMicroservicesMongoDBNoSQLPythonPyTorchTensorflow
About the role
- Implementation of production ready models in the areas of Natural Language Processing, Image Processing, and Machine Learning
- Work on rapid-prototypes, development, and implementation of scalable ML models in the development of the E2E product with the help of Machine Learning and Natural Language Processing
- Converting the prototype into algorithmic framework
- Coming up with algorithmic approaches to handle the problem statements
- Demonstrating the model performance and its value addition to the platform
- Deployment of the ML models into cloud
- Closely working with our Engineering / DevOps teams in implementing solutions at scale
- Monitoring production logs
- Root Cause on anomalies and fixing
Requirements
- Engineer AI - Datascience focus. Ability to write scalable code in Python and/or Java
- Hands on experience in TensorFlow / Pytorch with solid understanding of the Deep learning concepts
- Strong knowledge on Maths, Probability and Statistics
- Proficient in Natural Language Processing, Image processing and the mathematical concepts behind it
- Experience in productionizing and handling production environments
- Experience in deploying ML models into production
- Experience in NoSQL like MongoDB etc.
- Experience in multithreading and multiprocessing
- 3+ Years of experience in AI Engineering role
- Preferred degree in Computer Science, Mathematics or similar courses or fields
- Ability to communicate findings clearly to both technical and non-technical audiences and to effectively collaborate within cross-functional teams
- Working knowledge of agile framework and processes
- Desired Skills/Nice to have: Experience in Java programming
- Desired Skills/Nice to have: Experience in working in– Azure/AWS/ GCP with Docker and Kubernetes
- Desired Skills/Nice to have: Strong command in data modelling, software architecture and data structures
- Desired Skills/Nice to have: Experience in Microservices architecture