Key takeaways
- An AI engineer in 2026 builds products on top of models: RAG, fine-tuning, agents, evaluation and deployment. Training models from scratch is a different, rarer job.
- The roadmap is Python, one web framework, one deployed project per quarter, then RAG, then fine-tuning, then agents, with evaluation running through all of it.
- Your degree matters less than your repositories. A BCA with five shipped projects beats a B.Tech with none, and I say that as the BCA.
- Portfolio projects should solve a real problem for a real user and be live at a URL, with a README that a recruiter can read in two minutes.
- Salary numbers online are marketing. Anchor on the role and company tier, negotiate on evidence, and treat the first job as the one that buys the second.
What does an AI engineer actually do?
In 2026 an AI engineer, often titled GenAI engineer, applied AI engineer or LLM engineer, builds products on top of existing models: retrieval systems over company documents, fine-tuned models for narrow tasks, agents that call tools, evaluation harnesses that prove the system works, and the deployment, monitoring and cost control around all of it. Training foundation models from scratch is a research role at a handful of labs and is not what most job posts mean.
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My own week is the evidence. In the first half of 2026 I shipped five production AI applications at NextUpgrad: a RAG platform, a fine-tuning platform, a document intelligence service, a browser-only face recognition system and an OCR and speech workspace [10]. None involved training a model from scratch. All involved Python, an API framework, a database, Docker, tests, and a lot of evaluation. That is the job.
Nobody hired me for what I knew. They hired me for what I had shipped and could explain failure by failure. Pranjul Rathour
How do I become an AI engineer with no experience?
Build in public, in this order: learn Python well enough to write a small service; learn one web framework and deploy something live; build a RAG system over documents you care about; fine-tune a small model on a dataset you made; build an agent with one real tool; write an evaluation for each. Publish every project with a README, a live URL and a demo video. Apply with those links, not with a course certificate.
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That timeline is what I told 100+ first-years at the VSICS induction in July 2025, and it is the shape of my own path from a BCA in Kanpur to leading engineering at an agency and shipping production AI systems [11]. It is not fast. It is faster than any alternative I have seen work.
Can I become an AI engineer from BCA, or a tier-3 college?
Yes. I did a BCA at VSICS Kanpur, not an IIT B.Tech, and the interviews that mattered never asked about the college; they asked about RAG.NextUpgrad and what broke in it. Companies filter on degree when they have nothing else to look at. Give them something else: live projects, hackathon results, public writing, a GitHub that shows steady work. The degree becomes a footnote.
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What a BCA lacks compared with a B.Tech is usually maths depth and campus placement access. Fix the first with one solid linear algebra course and by reading papers with a notebook open; fix the second by going where the companies are, hackathons, communities, cold outreach, instead of waiting for a placement cell.
What skills does an AI engineer need in 2026?
Python fluently, including async and packaging. One API framework, FastAPI or similar. SQL and one database, plus a vector index. The LLM stack: prompting, RAG, fine-tuning with LoRA, tool calling and agents, evaluation. Deployment: Docker, a CI pipeline, logging and metrics, one cloud or platform. Enough maths to read a paper and reason about embeddings and loss curves. And the soft skill that decides senior roles: writing a clear spec and a clear README.
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The foundational papers are short and readable; the RAG paper [2] and the LoRA paper [3] are the two I ask every mentee to read in their first month, with Anthropic's agents guide as the practical companion [1].
Which programming language and courses should I start with?
Python, without debate, plus enough JavaScript or TypeScript to build an interface when a project needs one. For courses: the official Python tutorial [4], the FastAPI documentation [5], Hugging Face's free LLM and agents courses [6], fast.ai's practical deep learning course for intuition [7], and Andrew Ng's machine learning specialisation if you want the classical foundation [8]. All free or nearly. Spend more time building than watching.
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On certificates: I have never seen one change a hiring decision at SCULT INDIA or anywhere I have interviewed. I have seen a deployed project with a clear README change many.
What projects should I build for an AI engineer portfolio?
Five projects that together cover the stack: a RAG assistant over a real document set with measured recall; a fine-tuned small model with a base-versus-tuned comparison; an agent with one real tool and a safety boundary; a computer vision or speech application with a privacy decision explained; and one boring, useful web app for a real person or club. Each live at a URL, each with a README that states the problem, the decision you made, and the result.
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The RAG project is the one recruiters ask about most, because it is the one they are hiring for most. My longer write-up on which projects lead to interviews is on the portfolio [13].
How do I build a GitHub profile recruiters read?
Pin five repositories, each with a README that has the problem in one sentence, a screenshot or demo link, how to run it, and what you would do next. Commit steadily rather than in bursts. Delete or archive tutorial clones. Add a profile README with your role, city, and three links: portfolio, LinkedIn, email. Recruiters spend under two minutes; make those minutes land on finished things.
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How do I get an AI internship or a first job?
Apply where the work is visible: startups, agencies and small product companies that ship AI features, and remote roles that hire on portfolios. Cold email founders with one line about their product and a link to a project that resembles it. Enter hackathons, because judges and sponsors hire. Post one build a week on LinkedIn. Treat the first role as the one that produces the stories you need for the second.
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My first serious work came through a community I built, TechVerse Enclave, and through hackathons, not through a placement cell. That is not an argument against placement cells; it is an argument for not waiting for one.
What is the salary of an AI engineer in India?
I will not give you a number, because the numbers that circulate online are unmeasured and this page only reports what can be checked. What I can tell you from hiring and being hired: pay tracks the company tier and city far more than the title; a GenAI engineer at a funded startup in Bengaluru and one at a Kanpur agency are different bands for the same skills; remote roles for foreign companies pay more and hire on portfolios; and the fastest way to raise your number is a second offer, not a longer wait.
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Can I get a remote AI job from India?
Yes, and remote roles are where portfolios matter most, because the company cannot see your college and does not care. Target companies that already hire remotely, contribute to open-source projects they use, apply with a project that mirrors their product, and be ready for asynchronous, written communication, which is a skill you can practise by writing READMEs and posts. Time-zone overlap with Europe is India's structural advantage.
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How long does it take to become an AI engineer?
Twelve to eighteen months of steady building, alongside a degree, gets most people to a hireable portfolio: Python, one deployed app, RAG, a fine-tune, an agent, all live. Faster is possible with full-time focus; slower is normal with exams. The variable that matters is shipped projects per quarter, not hours of video watched.
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What are common AI engineer interview questions?
Explain a RAG system you built and what failed. Compare RAG with fine-tuning and say when you would choose each. Explain LoRA in one minute. Describe how you evaluated a system and what metric you chose. Design an agent for a task and name its guardrails. Estimate the cost of an LLM feature. Write a function under time pressure. The pattern: they want to know you have shipped and can reason about trade-offs, not that you memorised definitions.
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Is AI engineering a good career, and will AI replace AI engineers?
It is a good career for people who like building and are comfortable with tools changing every year. The models will keep getting better at writing code, which changes the job from typing to specifying, evaluating and integrating. Engineers who can define a problem, judge an output and ship a system will be more valuable, not less. Engineers whose only skill is producing code from a clear spec are the ones at risk, in AI and everywhere else.
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Where can I get help on this path?
Ask. I mentor students through TechVerse Enclave, run sessions at colleges, and answer real questions by email. The full roadmap article with resources is on the portfolio [12]. If your college wants a career session on GenAI engineering, or a workshop that ends with every student having a deployed project, email pranjulrathour41@gmail.com or use pranjulrathour.scult.in/invite.
Sources
- Anthropic, Building effective agents (2024)anthropic.com
- Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)arxiv.org
- Hu et al., LoRA: Low-Rank Adaptation of Large Language Models (2021)arxiv.org
- Python official tutorialdocs.python.org
- FastAPI documentationfastapi.tiangolo.com
- Hugging Face Learn courses (LLM, NLP, agents)huggingface.co
- fast.ai Practical Deep Learning for Coderscourse.fast.ai
- Andrew Ng, Machine Learning Specialization (Coursera / DeepLearning.AI)deeplearning.ai
- Docker documentationdocs.docker.com
- Pranjul Rathour, portfolio and projectspranjulrathour.scult.in
- From engineering student to GenAI engineer, Pranjul Rathourpranjulrathour.scult.in
- GenAI engineer roadmap 2026 for India, Pranjul Rathourpranjulrathour.scult.in
- AI portfolio projects that get interviews, Pranjul Rathourpranjulrathour.scult.in
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
