Zycus Off Campus Drive 2026
Zycus
Experience0–1 Year
QualificationB.E/B.Tech/B.Sc/BCA
Batch2025 / 2026
Key Responsibilities
- Assist in developing and deploying AI and Machine Learning models.
- Work on data preprocessing, cleaning and analysis.
- Support the development of NLP, computer vision or Generative AI solutions.
- Research new AI technologies, frameworks and tools.
- Help optimize model performance and accuracy.
- Collaborate with engineering and product teams on AI-driven features.
- Prepare project documentation, reports and presentations.
Skills & Eligibility
- B.E./B.Tech graduate or final-year student.
- Only 2025 or 2026 graduates are eligible according to the supplied description.
- 0–1 year of experience in AI/ML projects or internships.
- Strong interest in Artificial Intelligence and emerging technologies.
- Basic understanding of Machine Learning concepts.
- Basic understanding of Deep Learning concepts.
- Familiarity with Python .
- Exposure to libraries such as TensorFlow, PyTorch, Scikit-learn or Pandas .
- Knowledge of APIs.
- Understanding of Data Structures and Algorithms.
- Understanding of Generative AI or Large Language Models is an advantage.
- Good analytical and problem-solving skills.
- Strong communication and teamwork abilities.
- B.E./B.Tech graduate or final-year student.
- Only 2025 or 2026 graduates are eligible according to the supplied description.
- 0–1 year of experience in AI/ML projects or internships.
- Strong interest in Artificial Intelligence and emerging technologies.
- Basic understanding of Machine Learning concepts.
- Basic understanding of Deep Learning concepts.
- Familiarity with Python .
- Exposure to libraries such as TensorFlow, PyTorch, Scikit-learn or Pandas .
- Knowledge of APIs.
- Understanding of Data Structures and Algorithms.
- Understanding of Generative AI or Large Language Models is an advantage.
- Good analytical and problem-solving skills.
- Strong communication and teamwork abilities.
- Machine Learning is one of the core technical areas mentioned in the job description.
- Candidates should have a basic understanding of how machine learning models are developed and evaluated. Important concepts to revise include:
- Supervised and unsupervised learning.
- Classification and regression.
- Training and testing datasets.
- Feature engineering.
- Model evaluation.
- Overfitting and underfitting.
- Accuracy and other evaluation metrics.
- Basic model optimization.
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