AI Engineer (GenAI) fresher requirements (2026)
These are the published requirements FresherHub scores an AI Engineer (GenAI) against (role version 1). The weights are fixed in the role definition; AI never changes them.
The short answer
FresherHub checks a fresher AI Engineer (GenAI) against 14 requirements in 5 areas. The 4 must-haves: Python or TypeScript proficiency, LLM API integration, Embeddings & vector database and Deployed GenAI project. LLM integration and Retrieval (RAG) carry the most weight, at 25% of the readiness score each.
A requirement counts fully only when there is proof you can point to, such as a repository, a deployed project or a notebook. A skill you only list counts for much less.
How the score is weighted
| Area | Share of your score | Requirements |
|---|---|---|
| Fundamentals | 15% | 2 (1 must-have) |
| LLM integration | 25% | 3 (1 must-have) |
| Retrieval (RAG) | 25% | 2 (1 must-have) |
| Production | 20% | 4 (1 must-have) |
| Professional | 15% | 3 |
Within an area, each requirement has its own weight. Your readiness is the weighted sum, so the share shown on each requirement below is how much of the overall score it carries. How scoring works explains the formula.
Requirements, area by area
Fundamentals · 15% of your score
Python or TypeScript proficiency
Must-have9% of the overall score
Comfortable building applications in Python or TypeScript — either satisfies, since GenAI apps are commonly built in both ecosystems.
What counts as proof
- Resume lists Python or TypeScript under skills or experience.
- Public repositories show meaningful Python or TypeScript usage.
How to close the gap
- Ship a small app (Python or TypeScript) with clear modules and error handling
Git & version control
6% of the overall score
Regular, incremental commits and branch hygiene visible on GitHub.
What counts as proof
- Consistent recent contributions (not a single bulk dump).
How to close the gap
- Keep small, descriptive commits on your primary GenAI repo
LLM integration · 25% of your score
LLM API integration
Must-have10% of the overall score
Integrates an LLM provider API (OpenAI, Hugging Face, or similar) into an application — auth, request/response handling, streaming.
What counts as proof
- Resume claims LLM API integration experience.
- Repositories depend on an LLM provider SDK.
- Project calls an LLM API for real application behavior (chat, completion, classification).
How to close the gap
- Build one feature backed by a real LLM API call (not a hardcoded demo)
Prompt engineering
7.5% of the overall score
Designs and iterates on prompts (system prompts, few-shot examples, structured output) rather than ad-hoc one-off text.
What counts as proof
- Resume claims prompt engineering experience.
- Project shows versioned or documented prompts, not inline strings only.
How to close the gap
- Extract and document your system prompts with rationale for each choice
LLM orchestration framework (optional)
7.5% of the overall score
Uses LangChain, LlamaIndex, or a similar framework to compose chains/agents — optional, not mandatory, since raw API calls also satisfy the role.
What counts as proof
- Resume claims LangChain or LlamaIndex experience.
- Repositories depend on LangChain or LlamaIndex.
How to close the gap
- Refactor one multi-step LLM flow into LangChain or LlamaIndex
Retrieval (RAG) · 25% of your score
Embeddings & vector database
Must-have13.8% of the overall score
Generates embeddings and stores/queries them in a vector database (Pinecone, FAISS, ChromaDB, or Weaviate — any one satisfies).
What counts as proof
- Resume claims embeddings/vector database or RAG experience.
- Repositories depend on a vector database client.
- Project performs semantic search / retrieval over embedded content.
How to close the gap
- Add embeddings + a vector store to your strongest LLM project
End-to-end RAG pipeline
11.3% of the overall score
A project shows a complete RAG flow — document ingestion, chunking, retrieval, and grounded generation — not just a vector search demo.
What counts as proof
- Project documents the full RAG flow from ingestion to grounded answer.
How to close the gap
- Diagram and document your RAG pipeline end-to-end in the README
Production · 20% of your score
Evaluation & guardrails
6% of the overall score
Has some form of output evaluation or safety guardrails (eval harness, hallucination checks, content filtering, rate limiting) beyond eyeballing responses.
What counts as proof
- Project documents evaluation or guardrail measures for LLM output.
How to close the gap
- Add a small eval set and guardrail checks around your LLM output
App/API wrapper
5% of the overall score
Wraps the GenAI logic in a real app or API (FastAPI or Next.js) instead of leaving it as a script or notebook.
What counts as proof
- Resume claims FastAPI or Next.js for the GenAI app.
- Repositories depend on FastAPI or Next.js.
How to close the gap
- Wrap your GenAI project in a FastAPI or Next.js app with a real UI or API
Cost & latency awareness
4% of the overall score
Shows awareness of LLM cost/latency trade-offs — token usage tracking, caching, model selection, or response-time budgets.
What counts as proof
- Project documents cost or latency handling for LLM calls.
How to close the gap
- Document token/cost tracking and any caching in your GenAI project README
Deployed GenAI project
Must-have5% of the overall score
At least one GenAI project is deployed with a reachable URL or clear deploy docs.
What counts as proof
- Project has a live URL or deploy platform evidence.
How to close the gap
- Deploy your primary GenAI app and link it from GitHub/README
Professional · 15% of your score
GitHub project presentation
6% of the overall score
Strongest GenAI repo is evaluable quickly: README with architecture, model/provider choices, and setup.
What counts as proof
- Primary GenAI repo README covers architecture, providers/models used, and setup.
How to close the gap
- Rewrite your strongest GenAI project README for recruiters
- Add a GitHub profile README oriented to AI Engineer (GenAI)
Resume project positioning
5.3% of the overall score
Project bullets convey GenAI system difficulty and outcomes, not just 'used ChatGPT API'.
What counts as proof
- Resume project section names architecture/approach and measurable results, not generic tasks.
How to close the gap
- Rewrite GenAI project bullets for system difficulty and measured impact
LinkedIn role positioning
3.8% of the overall score
Headline/About and featured work signal AI Engineer (GenAI), not a generic student profile.
What counts as proof
- Headline or About names GenAI stack and target role.
How to close the gap
- Update LinkedIn headline to AI Engineer (GenAI) + stack
- Rewrite LinkedIn About with real GenAI projects and target role
Listing a skill vs proving it
Each requirement is scored by the best piece of evidence for it. A skill named in your resume's skills list is a claim and counts for 25%. Work we can see counts for more: observed 60%, demonstrated 80% and strong 100%. Read Am I job-ready? for a self-check you can do today.
A sensible order to work in
Programming Fundamentals
Python or TypeScript proficiency and Git hygiene.
LLM Integration & Prompting
Calling LLM provider APIs, designing prompts deliberately, and optionally an orchestration framework.
Retrieval-Augmented Generation
Embeddings, a vector database, and a complete RAG pipeline.
Production GenAI Engineering
Evaluation/guardrails, an app/API wrapper, cost/latency awareness, and shipping a deployed project — the gap that most limits fresher GenAI readiness.
Job Ready
Tailored resumes, portfolio, applications, and professional positioning.
Frequently asked questions
What does a fresher AI Engineer (GenAI) need to know?
FresherHub checks a fresher AI Engineer (GenAI) against 14 requirements in 5 areas. The 4 must-haves: Python or TypeScript proficiency, LLM API integration, Embeddings & vector database and Deployed GenAI project. LLM integration and Retrieval (RAG) carry the most weight, at 25% of the readiness score each. The areas and their weights: Fundamentals (15%), LLM integration (25%), Retrieval (RAG) (25%), Production (20%), Professional (15%).
Which requirement counts most for an AI Engineer (GenAI)?
Embeddings & vector database, worth 13.8% of the overall readiness score, and it is a must-have. Generates embeddings and stores/queries them in a vector database (Pinecone, FAISS, ChromaDB, or Weaviate — any one satisfies).
Does listing a skill on my resume count?
Only as a claim. A skill that appears only in your resume's skills list counts for 25% of a requirement. Proof we can see in your work, such as a public repository with tests or a deployed project, counts for 60% to 100%.
How do I check if I am ready for an AI Engineer (GenAI) job?
Run the free FresherHub readiness check: pick AI Engineer (GenAI), add your resume and connect GitHub. In about 3 minutes you get a readiness score, your top blockers and your next best action, checked against the requirements on this page. It does not guarantee a job; it shows what to fix first.
Is the AI Engineer (GenAI) readiness check free?
Yes. The readiness score, top blockers, top 3 next actions and a public portfolio page are free. Placement Sprint is a ₹299 one-time payment for 30 days of access, with no auto-renewal.
Check your AI Engineer (GenAI) readiness
Pick AI Engineer (GenAI), add your resume and connect GitHub. You get your readiness score against these requirements, your top blockers and the one thing to do next.