Career Advancements
Prepare yourself for real success.
Career Advancements
While technical skill development forms a core part of your journey, our platform also provides career counselling, expert-led workshops, project support, and opportunities for real-world collaboration—ensuring holistic growth for aspiring AI professionals.
Workshops
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Workshops
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Roles & Responsibilities
Designed for college students who want real-time learning from AI professionals.
Know
Roles & Responsibilities
Designed for college students who want real-time learning from AI professionals.
DATA COLLECTION & pREPARATION
Collecting data from sources such as web scraping and enterprise databases, ensuring the information is clean and relevant for artificial intelligence projects. After acquiring the data, the engineer applies feature engineering, stores data in appropriate systems like vector databases, and structures it for efficient retrieval and model training
Skill Required
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MODEL Development
Design and refine model architectures such as deep learning neural networks, leveraging pre-trained models and transfer learning to accelerate development and optimize performance. Throughout model development, they employ ML evaluation metrics, perform hyperparameter tuning, and implement advanced strategies like RAG (vector search combined with LLMs) to improve accuracy and efficiency.
Skill Required
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DEPLOY AI MODELS
Package models and export trained weights for serving through cloud platforms, ensuring reliable and scalable access. By leveraging containerization with Docker and automating through CI/CD pipelines, they integrate MLOps principles, monitor model drift, and uphold ethics by addressing bias and data privacy in production environments
Skill Required
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
DATA COLLECTION & pREPARATION
Collecting data from sources such as web scraping and enterprise databases, ensuring the information is clean and relevant for artificial intelligence projects. After acquiring the data, the engineer applies feature engineering, stores data in appropriate systems like vector databases, and structures it for efficient retrieval and model training
MODEL Development
Design and refine model architectures such as deep learning neural networks, leveraging pre-trained models and transfer learning to accelerate development and optimize performance. Throughout model development, they employ ML evaluation metrics, perform hyperparameter tuning, and implement advanced strategies like RAG (vector search combined with LLMs) to improve accuracy and efficiency
DEPLOY AI MODELS
Package models and export trained weights for serving through cloud platforms, ensuring reliable and scalable access. By leveraging containerization with Docker and automating through CI/CD pipelines, they integrate MLOps principles, monitor model drift, and uphold ethics by addressing bias and data privacy in production environments
Expert Connect Session
1-1 Expert Connect Session
Code Review
Career Counselling
