Data Scientists

Responsibility:
Provide AI software engineering knowledge and technical expertise to deliver
unique software and applications.
Productionalize stateoftheart research into maintainable and wellengineered AI
software.
Improve AI/Machine learning models by conducting experiments and analyzing
results to address business requirements.
Coordinate with various teams to solve problems efficiently and effectively.
Collaborate with other engineers and scientists on projects and ideas.
Mentor freshers and interns.
Qualification Requirements:
Educational:
Bachelor's degree with at least 7 years of experience or Master's with at least 4
years of experience. The final year project must involve the use of deep learning.
Must have a Computer Science Data Science Machine Learning or Computer
Engineering degree from a tier1 institute (IIT/IISc if studied in India) or a University
of top world ranking (as per QS).
Technical:
Industrial experience must be in AI/ML projects.
Exceptional Python programming skills.
Proficiency in deep learning frameworks such as TensorFlow / PyTorch.

Familiarity with training productionlevel deep learning models such as Feed
Forward neural networks CNN LSTM GANs Transformers etc.
Experience deploying efficient and optimized ML models with tensorrt onnx or
triton inference server frameworks with fast APIs.
Prior experience in building predictive models on large amounts of tabular data or
demonstrated experience in successfully solving ML problems in either NLP or CV.
Experience with Snowflake and Databricks is a plus.
A high level of attention to detail and the ability to produce accurate and consistent
engineering documentation.
Container Technologies: Kubernetes Docker.
Expertise in any one cloud platform: AWS Azure GCP.
Other:
Strong written and verbal communication skills in English are a must.
A desire to contribute to and maintain the company values and culture.
Demonstrated experience working collaboratively in crossfunctional teams.
Strategic thinking with the ability to work independently and in a group under tight
deadlines