ETDOX AI/ML Intern Recruitment 2026 – Overview
| Company | ETDOX |
| Role | AI/ML Intern |
| Job Type | Internship |
| Qualification | College students, University students, Freshers, Aspiring AI/ML enthusiasts, Candidates without professional experience |
| Work Mode | Remote / Work From Home |
The ETDOX AI/ML Internship is a remote, part-time opportunity for students, freshers, and aspiring AI/ML practitioners who want hands-on experience with Machine Learning, Python, data analysis, Generative AI, NLP, computer vision, and related technologies. Prior professional or internship experience is not required.
The internship duration is listed as 2–4 months, with monthly pay ranging from ₹2,000 to ₹10,000. Candidates can work remotely with a flexible schedule and are encouraged to demonstrate their skills through projects, coursework, GitHub repositories, hackathons, or personal work.
ETDOX AI/ML Internship Overview
| Particular | Details |
|---|---|
| Company | ETDOX |
| Role | AI/ML Intern |
| Opportunity Type | Internship |
| Work Mode | Remote / Work From Home |
| Schedule | Part-Time |
| Duration | 2–4 Months |
| Pay | ₹2,000–₹10,000 per month |
| Prior Experience | Not Required |
| Flexible Schedule | Available |
| Candidate Type | Students, Freshers, Aspiring AI/ML Practitioners |
About the AI/ML Intern Role
The internship focuses on practical applications of Artificial Intelligence and Machine Learning.
Selected interns will work with datasets, Machine Learning models, AI tools, experiments, and technical problem-solving while receiving guidance from the team.
The opportunity is designed for candidates who may not yet have professional AI/ML experience but can demonstrate strong fundamentals, curiosity, and willingness to learn.
Key Responsibilities
Selected interns may be responsible for:
- Assisting with Machine Learning model development
- Experimenting with different ML approaches
- Collecting datasets
- Cleaning and preprocessing data
- Analysing datasets
- Performing exploratory data analysis
- Identifying useful patterns in data
- Training Machine Learning models
- Evaluating model performance
- Improving existing models
- Experimenting with algorithms and features
- Supporting AI/ML research and projects
- Exploring relevant AI tools and frameworks
- Documenting experiments and results
- Collaborating with team members
- Solving technical problems
- Building practical AI/ML solutions
Eligibility Criteria
The opportunity is open to:
- College students
- University students
- Freshers
- Aspiring AI/ML enthusiasts
- Candidates without professional experience
The listing specifically states that prior internship or professional experience is not required.
Candidates should have an interest in Artificial Intelligence and Machine Learning and be willing to learn through practical projects.
Eligible Academic Backgrounds
Students from the following areas can apply:
- Computer Science
- Artificial Intelligence / Machine Learning
- Data Science
- Engineering
- Biotechnology
- Bioinformatics
- Mathematics
- Statistics
- Related fields
The listing also welcomes candidates who have learned AI/ML through:
- Online or offline courses
- Self-study
- College projects
- Hackathons
- Personal projects
Required Skills
Candidates should preferably have:
- Basic to intermediate Python knowledge
- Understanding of fundamental Machine Learning concepts
- Familiarity with NumPy
- Familiarity with Pandas
- Familiarity with Scikit-learn
- Familiarity with Matplotlib or Seaborn
- Basic understanding of data preprocessing
- Basic understanding of model evaluation
- Problem-solving skills
- Analytical ability
- Curiosity and willingness to learn
Good-to-Have Skills
Knowledge or exposure to the following is considered a plus but is not mandatory:
- Deep Learning
- PyTorch
- TensorFlow
- Generative AI
- Large Language Models
- Prompt Engineering
- RAG
- AI Agents
- Computer Vision
- Natural Language Processing
- SQL
- Databases
- Git
- GitHub
- FastAPI
- Deployment frameworks
- Docker
- Cloud platforms
Candidates do not need to know every technology listed above.
Projects and Practical Experience
ETDOX states that it values demonstrated ability and willingness to learn more than the number of internships on a candidate’s resume.
Applicants without professional experience may demonstrate their skills through:
- GitHub repositories
- College projects
- Personal projects
- Coursework
- Hackathon projects
- Other relevant technical work
Strong fundamentals, curiosity, problem-solving ability, and willingness to experiment are highlighted as important qualities.
Machine Learning Work
Interns may work across the Machine Learning workflow, including:
- Data collection
- Data cleaning
- Preprocessing
- Exploratory data analysis
- Feature experimentation
- Model training
- Model evaluation
- Performance improvement
The role gives candidates practical exposure to how models are developed and tested using real-world datasets.
Generative AI and Emerging Technologies
The listing also mentions several emerging AI areas as useful additional exposure.
These include:
- Generative AI
- LLMs
- Prompt Engineering
- RAG
- AI Agents
- Computer Vision
- NLP
These skills are considered beneficial rather than compulsory.
What Interns Can Gain
According to the supplied job description, interns may gain:
- Hands-on experience with practical AI/ML problems
- Exposure to real-world datasets
- Understanding of Machine Learning workflows
- Experience with AI tools and frameworks
- Guidance from experienced team members
- Portfolio-building project opportunities
- Exposure to Generative AI and other emerging AI fields
Pay
The ETDOX AI/ML Internship offers:
₹2,000–₹10,000 per month
The listing describes the opportunity as a student job, part-time role, fresher opportunity, temporary/contractual position, and internship.
Internship Duration
The stated duration is:
2–4 months
Location and Work Mode
The internship is listed as:
Remote / Work From Home
The source also mentions a flexible schedule.
How to Apply for ETDOX AI/ML Internship
Candidates interested in the ETDOX AI/ML Internship should prepare their resume along with relevant proof of practical work.
The job description specifically asks applicants to send:
- Resume
- GitHub profile, if available
- Portfolio, if available
- Links to relevant projects
Use the supplied application page:
Official Application Link: Apply Here
Students can also explore more internships, jobs, placement updates, and academic resources on NotesGallery.
Preparation Tips
The following are general suggestions based on the role and are not additional official requirements from ETDOX.
Candidates may consider:
- Revising Python fundamentals
- Practising NumPy and Pandas
- Reviewing Scikit-learn
- Practising exploratory data analysis
- Revising model evaluation metrics
- Preparing 1–2 Machine Learning projects
- Updating GitHub repositories
- Reviewing basic Deep Learning concepts
- Exploring Generative AI and LLM fundamentals
- Preparing to explain projects and technical decisions clearly
Frequently Asked Questions
What is ETDOX AI/ML Internship?
The ETDOX AI/ML Internship is a remote, part-time internship for students and aspiring AI/ML practitioners who want practical experience with Machine Learning, Python, datasets, AI tools, and emerging technologies.
Is prior experience required?
No. The listing clearly states that previous professional or internship experience is not required.
What is the pay for the internship?
The listed pay is ₹2,000–₹10,000 per month.
What is the internship duration?
The stated duration is 2–4 months.
Is the internship remote?
Yes. The position is listed as remote and includes work-from-home benefits.
Which students can apply?
Students from Computer Science, AI/ML, Data Science, Engineering, Biotechnology, Bioinformatics, Mathematics, Statistics, and related fields can apply.
What skills are required?
Candidates should have basic to intermediate Python knowledge, Machine Learning fundamentals, and familiarity with tools such as NumPy, Pandas, Scikit-learn, and Matplotlib or Seaborn.
Are Generative AI and LLM skills mandatory?
No. Generative AI, LLMs, RAG, AI Agents, Deep Learning, Computer Vision, and similar technologies are listed as good-to-have skills rather than mandatory requirements.
