AI/ML Engineering Intern
Help build AI features and automation: prepare data, train and evaluate models, and work with large language models alongside our AI team.
- Location
- Gurugram, India
- Experience
- Students & recent graduates
About the internship
AI and automation are at the heart of how Firmelist helps businesses work smarter. As an AI/ML Engineering Intern, you will support our team in building practical AI features — from cleaning and exploring data to training and evaluating models and building applications on top of large language models.
The focus is on applied machine learning: models that solve a real business problem, measured honestly, and built into software people use.
Part of the team from day one
Your internship is an opportunity to contribute, not just observe. You’ll work with your respective team, learn professional workflows, use role-relevant tools and take on responsibilities appropriate to your skills and experience.
What you’ll do
- Collect, clean and explore datasets with Python and pandas
- Train and evaluate machine-learning models with scikit-learn or PyTorch
- Build and test LLM-powered features such as retrieval-augmented generation and prompt pipelines
- Measure model quality with suitable metrics and document what you find
- Write reproducible experiments in Jupyter notebooks and turn useful ones into clean code
- Help integrate models into applications through APIs
- Share progress in team discussions and code reviews
What we’re looking for
Education
- Pursuing or recently completed a degree in computer science, data science, mathematics, statistics or a related field
Knowledge
- Good Python programming skills
- Understanding of core machine-learning concepts: training, validation, overfitting and common metrics
- Comfort with basic linear algebra, probability and statistics
Experience
- At least one ML or data project you can share — coursework, Kaggle or personal
Other
- A careful, evidence-driven approach: you check results before trusting them
Skills you’ll develop
- Python
- Machine learning fundamentals
- Data preparation
- Model evaluation
- Prompt engineering
- Retrieval-augmented generation
- Data analysis
- SQL
- Problem solving
Professional toolkit
- Python AI & Data
- Jupyter AI & Data
- pandas AI & Data
- scikit-learn AI & Data
- PyTorch AI & Data
- Hugging Face AI & Data
- SQL AI & Data
- Git Development
- GitHub Development
Tool access is provided according to your role and project needs.
What you’ll learn
Applied ML workflow
From a business question to data, model, evaluation and a working feature.
Working with LLMs
Prompting, retrieval, evaluation and the limits of large language models.
Honest evaluation
Choosing metrics, avoiding leakage and knowing when a model isn’t good enough.
Shipping AI
How models are packaged, served and monitored inside real software.
Eligibility
- Education
- Students or recent graduates in computer science, data science, mathematics, statistics or a related field.
- Experience
- No professional experience required — show us a project that demonstrates your skills.
What you get
Practical project experience
Work on real tasks for real projects instead of classroom exercises.
Team collaboration
Work alongside experienced team members and take part in project discussions.
Professional tools exposure
Work with the tools of an applied AI team: Python, Jupyter, pandas, scikit-learn, PyTorch and Hugging Face.
Regular feedback
Get feedback on your work so you know what to improve and how.
Portfolio-building work
Finish with work you can talk about, within confidentiality limits, in your next interview.
Your internship journey
-
1
Onboarding
Meet your team, set up your tools and learn how we plan, review and ship work.
-
2
Learning
Learn our data and AI stack, and reproduce an existing experiment.
-
3
Contributing
Run experiments, prepare data and evaluate models with the team.
-
4
Taking ownership
Own a well-defined piece of an AI feature, from experiment to working prototype.
-
5
Growing
Review what you achieved with your team, and plan the skills to build next.
Where this can lead
Applied AI skills open doors across engineering and data roles. Here is a common path for ML engineers.
-
Step 1
AI/ML Intern
-
Step 2
Junior ML Engineer
-
Step 3
ML / AI Engineer
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Step 4
Senior AI Engineer
Frequently asked questions
Apply for this internship
Tell us about yourself. It takes about five minutes, and we read every application.
- Position
- AI/ML Engineering Intern
AI/ML Engineering Intern
Apply now