This article outlines the AI/ML internship role, detailing responsibilities and candidate requirements that will be covered in depth below. It highlights hands-on tasks such as designing machine learning systems, building Python algorithms for data clean-up, implementing NLP tools like NLTK and BERT, conducting model testing and statistical analysis, and advising leadership on AI/ML strategy and policy.
Responsibilities and Core Deliverables
- Design machine learning systems and Python algorithms for data clean-up:
The intern will create machine learning architectures and develop Python-based algorithms focused specifically on cleaning and preparing data. This involves implementing automated data clean-up routines and integrating them into ML systems so datasets are suitable for training and evaluation.
- Research and implement machine learning algorithms and tools along with NLP, NLTK, BERT, Command R etc.:
Research tasks require evaluating relevant algorithms and tools and implementing them as appropriate. Work will include hands-on implementation with NLP technologies such as NLTK and BERT and other specified tools (for example, Command R) to support project objectives.
- Manage and direct research and development processes to meet the needs of our AI strategy:
The role includes coordinating R&D activities to ensure alignment with the organization’s AI strategy. This means guiding experimentation, prioritizing development tasks, and steering research efforts toward strategic goals.
- Develop machine learning applications in alignment with project requirements and business goals:
Development work focuses on building ML applications that meet explicit project requirements and support broader business objectives. Deliverables should reflect functional needs and contribute to measurable business outcomes.
- Perform machine learning tests and statistical analysis to fine-tune the machine learning models:
Testing and analysis are integral to improving model performance. The intern will run machine learning tests and apply statistical analysis to iteratively fine-tune models, ensuring they meet performance expectations.
- Select appropriate datasets and train systems and retrain as necessary:
Responsibility includes identifying suitable datasets for training, executing training pipelines, and retraining systems when required to maintain or improve model effectiveness.
- Advise leaders on technology, strategy, and policy issues related to AI/ML:
The intern will provide informed input to leadership on technology choices, strategic directions, and policy considerations relevant to AI/ML initiatives, supporting decision-making processes.
Requirements, Skills and Compensation
- Experience with developing and implementing AI algorithms:
Candidates must demonstrate prior experience in developing and putting AI algorithms into practice, indicating readiness for hands-on responsibilities.
- Excellent problem-solving skills and ability to work with large datasets:
The role demands strong analytical skills and comfort handling sizeable datasets to support model development, testing, and dataset selection.
- Knowledge of relevant programming languages such as Python:
Proficiency in Python is required for algorithm development and data-cleanup scripting as described in the responsibilities.
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field:
Applicants should hold a relevant undergraduate or graduate degree aligning with the technical nature of the internship.
- Experience in AI/ML projects and familiarity with machine learning frameworks is a plus:
Past involvement in AI/ML projects and exposure to ML frameworks will strengthen candidacy, though not strictly mandatory.
- Knowledge of version control systems (such as Git) and agile development methodologies:
Familiarity with version control and agile practices is expected to support collaborative development and iterative R&D processes.
- Working knowledge of AWS EC2 and AWS Lambda is a plus (not mandatory):
Experience with AWS EC2 and AWS Lambda is beneficial but optional for applicants.
- Internship Stipend:
Rs. 12000 /- per month.
Conclusion
This AI/ML internship combines practical engineering tasks—designing ML systems, Python-based data clean-up, NLP implementations, model testing and retraining—with strategic activities like managing R&D and advising leadership. Candidates should meet the academic and technical requirements listed, with additional familiarity in related tools considered advantageous. The internship offers a stipend of Rs. 12000 per month and real-world exposure to AI/ML initiatives.
