Job Market · India · 2026

What Companies Are Actually Hiring For: Entry-Level AI Roles

SECTION 01Must-have skills for entry-level AI roles

Based on analysis of 2026 job postings from Keywords Studios, Accenture, Novartis, CGI, and other leading employers, here are the skills employers are actually asking for :

Essential Technical Skills

• Python: The most important programming language for AI roles . You should be comfortable with Python fundamentals, data structures, and libraries like NumPy and pandas .
• Machine Learning Fundamentals: Understanding of supervised and unsupervised learning, model evaluation, and basic algorithms .
• Generative AI & LLMs: Companies are actively hiring freshers who understand LLMs, prompt engineering, and RAG .
• Natural Language Processing (NLP): Tokenization, embeddings, text classification, and semantic search .
• SQL: Basic knowledge of SQL databases and data querying .
• Git & Version Control: Understanding of Git/GitHub for collaboration .

High-Demand Frameworks & Tools

• LLM Frameworks: LangChain, LlamaIndex, or similar frameworks for building AI applications .
• AI/ML Frameworks: PyTorch or TensorFlow .
• Vector Databases: Pinecone, Weaviate, Qdrant, or ChromaDB .
• FastAPI: Building APIs and AI services .
• Docker: Containerization and deployment .
• Hugging Face Transformers: For working with pre-trained models .

Critical Soft Skills

• Communication: The ability to explain technical concepts clearly .
• Problem-Solving: Strong analytical and problem-solving skills .
• Curiosity and Learning Mindset: A passion for AI and emerging technologies .
• Collaboration: Ability to work in a team environment .

SECTION 02Entry-level AI roles in India

Based on 2026 job postings, here are the most common entry-level AI roles :

SECTION 03Salary benchmarks for entry-level AI roles

Based on data from Naukri.com, foundit, and job postings :

SECTION 04Real job requirements from actual postings

Example 1: AI Engineer (Fresher/Entry-Level) at Keywords Studios (Pune)

• Core Skills: Python, NLP, Generative AI, MLOps
• Key Responsibilities: Develop AI/ML applications, build NLP pipelines, implement RAG systems, design prompts, build APIs with FastAPI
• Frameworks: PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex
• MLOps: Docker, Git/GitHub, CI/CD, Kubernetes (basic)
• Databases: SQL, vector databases (Pinecone, Weaviate, Qdrant)
• Preferred: Hands-on projects in AI/ML, experience with chatbots or NLP applications, cloud platform familiarity (AWS, GCP, Azure)
• Stand-out projects: AI chatbot using LLMs, RAG-based QA system, AI agent using LangChain, document search platform, end-to-end ML deployment with Docker/Kubernetes

Example 2: Junior Data Scientist / AI Engineer at Accenture

• Core Skills: Python (pandas, NumPy, scikit-learn), TensorFlow/PyTorch, SQL, data visualization (Matplotlib, Seaborn, Power BI, Tableau)
• Generative AI: GPT, Stable Diffusion, Llama, transformers, diffusion models, LLMs
• Key Responsibilities: End-to-end data and AI projects—data collection, preprocessing, model development, evaluation, and deployment
• Education: Recent graduate with hands-on academic or project experience in data science, AI, or ML
• Locations: Mumbai, Bangalore, Gurgaon

Example 3: AI Developer (Apprentice/Fresher) at CGI (Bangalore)

• Core Skills: Generative AI, LLMs (GPT), Microsoft Azure AI Services, Azure OpenAI, Cognitive Services, Azure Functions
• Key Responsibilities: Develop, deploy, and fine-tune Generative AI applications
• Qualifications: B.Tech in CSE/IT/ECE (2024/2025 pass-outs, 60% throughout academics)
• Skills: APIs, cloud tools (Azure Portal), Git, prompt engineering, responsible AI practices

Example 4: Associate Analyst (Data Annotation) at GlobalLogic (Gurgaon)

• Role: Manual labeling of data (text, audio, video, images) for AI model development
• Requirements: Bachelor's degree in any discipline, strong English communication, attention to detail
• Skills: Basic computer skills, MS Office/Google Suite, ability to work in rotational shifts
• What they offer: Full training—no prior data annotation knowledge required
• Note: This is an accessible entry point into the AI industry for graduates from any background

Entry-Level AI Job-Ready Checklist:

Technical:
☐ Python (NumPy, pandas, scikit-learn)
☐ Machine Learning (supervised, unsupervised)
☐ Generative AI (LLMs, prompt engineering, RAG)
☐ NLP (tokenization, embeddings, text classification)
☐ SQL (basic queries)
☐ Git & version control
☐ Docker (basic understanding)
☐ FastAPI or Flask

Frameworks:
☐ PyTorch or TensorFlow
☐ Hugging Face Transformers
☐ LangChain or LlamaIndex

Projects:
☐ AI Chatbot using LLMs
☐ RAG-based Question Answering System
☐ AI Agent using LangChain
☐ End-to-end ML deployment with Docker

Soft Skills:
☐ Communication
☐ Problem-solving
☐ Curiosity and learning mindset

Interview:
☐ Mock interviews (10+)
☐ Technical coding practice
☐ Case study practice

Stand-out projects for entry-level AI roles:

1. AI Chatbot using LLMs
   - Build a conversational AI assistant
   - Use OpenAI API, Claude, or Gemini
   - Deploy with FastAPI

2. RAG-based Question Answering System
   - Implement Retrieval-Augmented Generation
   - Use vector databases (Pinecone, Qdrant)
   - Build a document search platform

3. AI Agent using LangChain
   - Build an agent that can reason and act
   - Integrate with tools and APIs
   - Show workflow automation

4. End-to-end ML Deployment
   - Train a model with PyTorch/TensorFlow
   - Containerize with Docker
   - Deploy on cloud (AWS, GCP, Azure)

These projects appear in job postings as "Nice-to-Have" .

SECTION 05How to become job-ready for entry-level AI roles

1. Build Strong Python Fundamentals
   Python is the foundation for all AI roles . Focus on data structures, algorithms, and libraries like NumPy and pandas .
2. Learn Machine Learning & Generative AI
   Understand ML fundamentals, then dive into GenAI—LLMs, prompt engineering, RAG, and agentic AI .
3. Build Real Projects
   Employers care more about what you have built than what you have studied . Build at least 2-3 real projects: an AI chatbot, a RAG-based QA system, or an AI agent .
4. Learn MLOps & Deployment
   Understanding Docker, Git, and basic CI/CD is a significant advantage .
5. Practice Communication
   Practice explaining technical concepts clearly .
6. Do Mock Interviews
   Practice technical coding interviews and case studies .

SECTION 06Common mistakes that get candidates rejected

SECTION 07Interview Q&A — entry-level AI roles

SECTION 08Test yourself — entry-level AI roles

SECTION 09Frequently asked questions

SECTION 10Continue from here