Proficient in developing a dynamic web app with Django framework following best coding practices
Job Description
Job description
Proficient in developing a dynamic web app with Django framework following best coding practices
Basic architecture design knowledge in building scalable web app
Code deployment on production server
Knowledge of security and data protection
Integration of data storage solutions
Understanding of fundamental design principles behind a scalable application
Good knowledge in developing RESTful APIs for the backend system and comfortable in using Git version control
Proficient understanding of database modelling and query optimisation and good at work around them
Fluency with the command line interface
Familiarity with Linux like operating systems
Good understanding about Data Structures and Algorithms
Familiarity with optimising web application to improve the performance
Proficient understating of data modelling and entity modelling
Knowledge of any other web related technology is a bonus
Responsibilities:
Develop and optimise application
Debug code, code comments and clear code
You will be responsible for developing a new application and adding new enhancements and features to the existing applications ensuring performance, responsiveness, and availability to the users
Optimise application for maximum speed, scalability and availability
Specializes in retrieval-augmented generation architecture — building the search, indexing, and retrieval layer that grounds LLM outputs in accurate, up-to-date information.
Works on pretraining and post-training of foundation models — architecture experiments, training efficiency, alignment techniques (RLHF, DPO), and scaling research, often in collaboration with research scientists.
Owns model development, training, and quality — building and improving the ML models (not just LLM wrappers) that power a product, from data pipeline to production deployment.
Owns the compute infrastructure that trains and serves AI models — GPU cluster management, distributed training pipelines, and inference serving at scale. A natural pivot for DevOps/backend infra engineers.
