Can I get a Gen AI job with no experience?

Yes. Many companies hire for entry-level Gen AI positions with 0 to 2 years of experience. What they look for instead of experience is: demonstrable Python skills, understanding of LLM concepts, 2 to 3 portfolio projects showing you can build with Gen AI tools, and enthusiasm for the field. Internships, open-source contributions, and hackathon projects all count as relevant experience.

What should I put on my resume for an entry-level Gen AI job?

Lead with projects, not education. List 2 to 3 Gen AI projects with brief descriptions of what you built, which tools you used (LangChain, RAG, vector databases), and the outcome. Include a GitHub link. List relevant skills (Python, LLM, RAG, LangChain) prominently. Mention any relevant courses or certifications. Keep it to one page.

Which companies hire entry-level Gen AI engineers?

IT services companies (Accenture, Capgemini, Infosys, Wipro, TCS) are the largest employers of entry-level Gen AI talent in India. They hire in volume and provide training. Startups hire fewer juniors but offer faster learning. Product companies and GCCs occasionally hire entry-level but are more competitive. Apply broadly across all three categories.

Entry Level Gen AI Jobs: How to Land Your First AI Role in 2026

The Entry-Level Gen AI Opportunity

There is a common misconception that Gen AI jobs require years of ML research experience. That was true for machine learning engineer roles five years ago, but the Gen AI field has created an entirely new category of engineering work that is far more accessible. Building applications with LLMs, RAG, and AI agents is closer to software engineering than to research, and companies need engineers at all experience levels.

In 2026, entry-level Gen AI job postings have grown by over 200 percent year-over-year. Companies are building Gen AI teams from scratch and cannot fill positions fast enough. If you have the right skills and portfolio, breaking in is more achievable than you think.

What Entry-Level Gen AI Roles Look Like

Entry-level Gen AI positions typically have titles like Junior AI Engineer, Associate AI Engineer, Gen AI Developer, or AI/ML Engineer (0 to 2 years). The work involves:

Building and maintaining RAG pipelines for internal or customer-facing applications
Integrating LLM APIs (OpenAI, Anthropic, Google) into backend services
Writing and optimizing prompts for specific use cases
Building proof-of-concept AI features and presenting results to stakeholders
Evaluating LLM outputs and improving system accuracy
Contributing to shared AI infrastructure and tooling

Notice that none of these require training models from scratch or publishing research papers. This is applied engineering work, and strong software engineering fundamentals combined with Gen AI-specific knowledge are what gets you hired.

The Skills Employers Actually Screen For

Must-Have Skills

Python: Clean, readable, well-structured Python code. Comfort with async patterns, API calls, and package management.
LLM Fundamentals: Understanding what LLMs are, how they work at a conceptual level, and their limitations. You do not need to know the math behind attention mechanisms, but you should understand concepts like tokenization, context windows, and temperature.
RAG: Ability to build a basic RAG pipeline: load documents, chunk them, generate embeddings, store in a vector database, and retrieve relevant context for queries.
API Integration: Experience calling LLM APIs and handling responses. Understanding rate limits, error handling, and cost management.

Strong-to-Have Skills

LangChain or LlamaIndex: Hands-on experience with at least one major framework.
Prompt Engineering: Systematic prompt design, evaluation, and iteration.
Git and GitHub: Version control fluency. Employers will look at your GitHub profile.
Basic web development: Ability to build a simple API or web interface for your AI applications (FastAPI, Streamlit, or Next.js).

Building a Competitive Portfolio

Your portfolio is the single most important factor in getting hired for an entry-level Gen AI role. Here is what makes a portfolio stand out:

Project 1: RAG Application

Build a RAG chatbot over a non-trivial document set. Choose a domain you find interesting: medical research papers, legal documents, cooking recipes, or technical documentation. Deploy it with a web interface. This project demonstrates the most in-demand Gen AI skill.

Project 2: AI Agent

Build an AI agent that uses tools to accomplish tasks. For example, an agent that can search the web, analyze data from a CSV file, and generate a report. Use LangGraph or CrewAI. This shows you understand the cutting-edge of Gen AI development.

Project 3: Something Unique

Build something that shows your personality and creativity. A Gen AI-powered game, a tool that solves a problem you personally face, or a creative application of AI. This project is what makes you memorable in a stack of applications.

Portfolio Presentation

Every project needs a detailed README with architecture diagram, setup instructions, and a demo link or screenshots
Write clean, well-organized code with meaningful variable names and logical structure
Include a blog post or video walkthrough for at least one project
Deploy at least one project to a live URL (Vercel, Railway, or Streamlit Cloud are free)

Where to Find Entry-Level Opportunities

IT Services Companies (Highest Volume)

Companies like Accenture, Capgemini, Infosys, Wipro, and TCS are building dedicated Gen AI practices and hiring hundreds of entry-level engineers. These roles offer structured training, exposure to diverse projects, and a pathway to specialization. Compensation typically ranges from 8 to 15 LPA.

Startups (Fastest Learning)

AI startups hire fewer juniors but offer an accelerated learning curve. You will work directly with senior engineers, ship to production faster, and gain breadth of experience quickly. Look for startups that have raised Series A or later funding, as they can afford to invest in training junior engineers.

Internship to Full-Time Pipeline

Many companies offer Gen AI internships (3 to 6 months) that convert to full-time roles. This is often the easiest path in because the hiring bar for interns is lower, and you get to prove yourself on the job. Apply aggressively to internship postings even if you are targeting full-time employment.

Application Strategy

Apply broadly: Submit 5 to 10 applications per week across services companies, startups, and GCCs. Do not wait for the perfect role.
Customize each application: Mention the specific company and role. Reference a project that is relevant to their business.
Follow up: After applying, find the hiring manager on LinkedIn and send a brief note with a link to your most relevant project.
Prepare for technical screens: Practice building a simple RAG application from scratch in under 2 hours. Many companies use this as a take-home assessment.
Keep building: Continue adding projects to your portfolio while job searching. Each new project is another conversation starter and proof of your growth.

Your 90-Day Action Plan

If you are starting from scratch, here is a realistic 90-day plan to become hire-ready for entry-level Gen AI roles:

Days 1 to 30: Complete Python fundamentals and one Gen AI course. Build your first RAG project.
Days 31 to 60: Complete LangChain Academy courses. Build your agent project. Start applying to internships.
Days 61 to 90: Build your unique project. Polish all READMEs. Write one blog post. Apply to 30+ roles. Prepare for interviews.