This program is designed for candidates who want to prepare for AI Developer / AI Engineer / ML Engineer / Generative AI Developer roles.

This is NOT a basic Python or introductory AI internship

Candidates should be prepared to learn and work across the complete AI development stack. Knowledge of only one or two tools is not sufficient for this career pathway.

COMPLETE AI DEVELOPER SKILL STACK

Candidates will be trained across the following industry-relevant technologies and development areas:

1. Programming & Software Engineering

- Advanced Python
- Object-Oriented Programming
- Data Structures & Algorithms fundamentals
- Exception Handling
- Modular & Production-oriented Python
- Virtual Environments & Package Management
- VS Code / Jupyter / Google Colab
- Debugging & Testing fundamentals

2. Data & Database Engineering

- NumPy
- Pandas
- Data Cleaning & Preprocessing
- Matplotlib & Seaborn
- SQL
- PostgreSQL
- Database fundamentals
- Working with structured and unstructured data

3. Machine Learning

- Scikit-learn
- Supervised & Unsupervised Learning
- Feature Engineering
- Model Selection
- Model Evaluation
- XGBoost / Gradient Boosting
- ML pipelines
- Model interpretation

4. Deep Learning & AI

- PyTorch
- Neural Networks
- CNN
- Computer Vision
- NLP fundamentals
- Transfer Learning
- Model training & evaluation
- Hugging Face ecosystem

5. Generative AI & LLM Engineering

- Generative AI fundamentals
- Large Language Models (LLMs)
- Prompt Engineering
- Context Engineering
- OpenAI / Gemini / Claude APIs
- Hugging Face Transformers
- Embeddings
- Tokenization
- Structured Outputs
- Function / Tool Calling
- Local LLMs with Ollama

6. RAG & Vector Intelligence

- Retrieval-Augmented Generation (RAG)
- Document processing
- Chunking strategies
- Embeddings
- Semantic Search
- Vector Databases
- FAISS
- ChromaDB
- Pinecone / equivalent vector platforms
- Retrieval evaluation
- RAG optimization

RAG is no longer something we should present as an optional “advanced topic”; it is one of the major patterns used for enterprise GenAI applications.

7. AI Agents & Modern AI Application Development

- AI Agents
- Agentic AI
- Tool Calling
- Multi-step AI workflows
- LangChain
- LangGraph
- LlamaIndex
- Agent memory & state
- MCP fundamentals
- AI automation workflows

This is important because modern AI development is moving beyond simple chatbot/API demonstrations toward agentic applications and tool-using systems.

8. AI APIs & Backend Development

- REST APIs
- FastAPI
- API development
- Authentication fundamentals
- Connecting AI models to applications
- Database + AI integration
- AI application backend development

FastAPI is particularly useful here because it connects the Python/AI layer with deployable applications rather than leaving students with notebook-only projects.

9. MLOps / LLMOps

- MLflow
- Model Versioning
- Experiment Tracking
- Model Deployment
- Model Monitoring
- LLM Evaluation
- Prompt / response evaluation
- Logging
- Observability fundamentals




- AI application performance monitoring

10. Deployment & Cloud

- Git
- GitHub
- Docker
- CI/CD fundamentals
- AWS
- Cloud deployment
- AI/ML cloud services
- Model/API deployment
- Basic Kubernetes concepts

Docker, cloud and deployment are critical because an AI Developer needs to move beyond “my model works in Jupyter” to “my AI application can actually be deployed and consumed.”

11. AI Evaluation, Security & Responsible AI

- LLM evaluation
- Hallucination detection
- Guardrails
- Prompt injection awareness
- Output validation
- Data privacy fundamentals
- AI security basics
- Responsible AI
- Monitoring & error analysis

12. Production-Level AI Projects

Candidates will work on projects that combine multiple components rather than isolated mini-projects.

Examples:

- RAG-based AI applications
- AI chatbots
- AI agents
- Computer Vision applications
- Document Intelligence systems
- LLM-powered applications
- AI APIs using FastAPI
- AI applications connected to databases
- Deployed AI applications
- End-to-end ML/AI projects

THE EXPECTED AI DEVELOPER JOURNEY

The objective is not:

Python → Certificate → Internship

Instead, the target is:

Python → Data → ML → Deep Learning → PyTorch → GenAI → LLMs → RAG → Vector DB → Agents → APIs → MLOps/LLMOps → Docker → Cloud → Deployment → Portfolio → Interview Preparation

This is the level of technical breadth we want candidates to understand before applying for modern AI Developer-oriented career opportunities.

IMPORTANT FOR APPLICANTS

This opportunity is not intended for candidates looking only for a short-term certificate or a basic free internship.

Candidates applying should understand that modern AI Developer roles require a broad technical foundation.

If you are looking to build a serious career in AI/ML and are prepared to invest the required time in learning, practical work and projects, you are encouraged to apply.

This is a career-development/internship pathway and does not guarantee employment or placement.

Pay: ₹1,000.00 - ₹2,000.00 per month

Application Question(s):

- Have you already completed any course/training in Data Science, AI/ML, Python, or a related technology field?

- Yes – Data Science
- Yes – AI/ML
- Yes – Python / Full Stack
- Yes – Multiple related courses
- No – I have not completed any formal training

- Which of the following technologies/domains have you already learned or worked with?

Select all that apply — Mandatory
- Python
- NumPy / Pandas
- SQL
- Data Analysis
- Statistics
- Machine Learning
- Scikit-learn
- Deep Learning




- TensorFlow / PyTorch
- NLP
- Computer Vision
- Generative AI
- LLMs
- Prompt Engineering
- RAG
- Vector Databases
- LangChain / LangGraph
- AI Agents
- FastAPI / REST APIs
- Git / GitHub
- Docker
- Cloud / AWS / Azure
- MLOps / MLflow
- None of the above

- Have you completed any technical projects using the technologies you mentioned above?

- Yes – Multiple end-to-end projects
- Yes – 1–2 projects
- Only academic/minor projects
- Only tutorials/practice projects
- No project experience yet

- The AI Developer career pathway requires exposure to a broad technology stack — including Python, Data Handling, SQL, ML, Deep Learning, PyTorch/TensorFlow, GenAI, LLMs, RAG, Vector Databases, AI Agents, APIs, Git, Docker, Cloud and MLOps/LLMOps.

Do you currently have working knowledge of most of these areas?
- Yes – I have working knowledge of most of them
- I know some of them but need training in the remaining areas
- No – I am starting from the basics

- If you do not currently have the required technical skills, are you genuinely interested in undertaking additional structured training to become job-ready in this field?

- Yes – I am serious about building an AI/ML career and willing to undergo the required training
- Yes – but I need to understand the program first
- No – I am looking only for an internship

- If your current technical skills do not meet the requirements for the internship, are you willing to undertake additional structured training to build the required skills?

- Yes – I am serious about building a career in AI/ML and willing to undergo the required training
- Yes – I would like to understand the training pathway first
- No – I am looking only for an internship

- Additional training, if recommended based on your assessment, may involve a program fee. If you are found suitable for the career pathway, are you willing to consider investing in the required training?

- Yes – I am willing to invest in the required training
- I would like to understand the program and fee structure first
- No – I am looking only for a free internship

- In a real software/AI company, technologies, frameworks, tools and development practices keep changing. As a working qualified, would you be willing to continuously learn and adapt to new technologies whenever your role or project requires it?

- Yes – I am comfortable continuously learning and adapting to new technologies
- Yes – if it is required for my project or role
- I prefer to work mainly with technologies I already know
- No – I prefer a fixed technology stack

- How serious are you about building a long-term career in AI/ML/Data Science?

- Very serious – I want to build this as my career
- Serious – I am actively preparing for an IT career
- Interested – I am still exploring
- Just looking for internship/certificate opportunities

Work Location: In person

📌 AI/ML Internship – Python, Data Science & Generative AI (Palarivattom)
🏢 Singularis Software Technologies
📍 Palarivattom