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100 Job Strategies
100 Job Strategies · 99 of 100
Don't tell companies you know AI. Pick the exact model they're already running and build inside it.
In short
Instead of applying to hundreds of postings and claiming generic AI skills, freshers can pick one technology companies are already using — like Meta's Llama — and build a job search entirely around its ecosystem: the companies, the recurring skills in their postings, a production-style project solving their actual problem, and open-source contributions that prove you can work in it for real.
A fresher's resume lists Python, AI and LLMs under Technical Skills, next to two hundred applications and zero callbacks. Every other resume in the pile says the same three words.
Across town, another fresher found fifty companies publicly running Meta's Llama for customer support, picked one recurring use case — RAG over a knowledge base — and built the exact system those companies need: retrieval, citations, an evaluation set, latency logs, a deployed API.
They didn't learn AI. They learned what fifty specific employers are already paying engineers to build, and then they built it.
Generic AI skills on a resume are unverifiable and identical to everyone else's. A technology ecosystem is not — it comes with a finite, discoverable set of companies, a recurring set of skills pulled straight from their own job postings, and a visible community of engineers and maintainers already working in it.
That structure turns a vague "I know AI" into a specific, checkable claim: this company uses Llama for this use case, needs these exact skills, and here is a project that demonstrates all of them together, deployed the way their engineers deploy things.
It also changes the outreach. "Please give me a chance" asks for charity. "I noticed your team's RAG use case, built the same system, here's the repo and the eval results" hands over evidence. The strategy generalises to any technology with a real company ecosystem — Kubernetes, Snowflake, PyTorch, React — the technology is just the entry point into the companies, people and problems.
Choose something companies are already actively using, not something you want to learn in the abstract — e.g. Meta's Llama.
Don't just collect names — research why each one adopted it, what product or internal system it powers, what roles they hire for, and the technical problems their engineers solve: RAG, agents, inference, fine-tuning, evaluation, latency, security, deployment, vector databases, APIs, cloud infra.
Pull current and past postings from those companies and list the skills that repeat — Python, FastAPI, AWS, Docker, Kubernetes, PyTorch, RAG, vector databases, LangChain, model evaluation, LLM inference.
Pick a single realistic direction the postings point to — e.g. customer-support automation — and build the real thing: Llama plus RAG over a knowledge base, source citations, graceful handling of unanswerable questions, an API, an evaluation set for accuracy and hallucinations, latency and failure logging, deployed on the stack those jobs actually ask for.
Clean code, architecture diagram, setup instructions, API docs, screenshots, a live demo if possible, and a README that explains engineering decisions, limitations, evaluation results, cost, latency and what you'd improve in production.
Find active open-source libraries, frameworks, integrations, docs repos, inference tools, RAG projects and evaluation tools around the technology, and send small, genuine improvements — doc fixes, tests, bug fixes, examples, reproducible issue reports.
Find engineers, tech leads and recruiters at those companies on LinkedIn, GitHub, X, developer communities and events. Study what they're actually building before you write to them.
Skip "I'm a fresher, please refer me." Say what you noticed about their use case, what you built that matches it, and share the documented project with a one-line explanation of the specific problem it solves.
As you talk to more companies and read more postings, fold what you learn back into the project so it keeps tracking real engineering problems, not your first guess at them.
If a posting asks for Python, RAG, AWS, Docker and LLM experience, the resume should show your Llama project using exactly those — implemented RAG, deployed on AWS, containerized with Docker — not a bullet list of skill words.
The strategy isn't learn the technology, then apply for jobs — it's find companies using it, study their real use cases, identify the skills that keep repeating, build the matching project, publish it properly, contribute to the ecosystem, reach the engineers working on the same problems, and use that proof of work to get the interview.
No — the mechanics are technology-agnostic. The same sequence works for Kubernetes, React, AWS, Snowflake, Databricks, PyTorch or PostgreSQL. Pick whatever technology has a real, discoverable company ecosystem behind it; the technology is the entry point, not the point.
Lower the bar to whatever number you can genuinely research well — even 15-20 companies studied properly beats 100 collected as names only. The research depth matters more than hitting the count.
It trades volume for signal. Hundreds of generic applications compete on nothing; one well-documented project matched to real postings gives a recruiter or engineer something concrete to say yes to, and it compounds — the same project supports every application in that ecosystem.
Small is fine — a documentation fix, a test, a reproducible bug report or a working example. The point isn't the size, it's showing you can operate inside someone else's codebase and process, which a solo project can't demonstrate.
