Computer vision in AI is the branch of artificial intelligence that trains machines to interpret and analyse visual data such as images and videos. It combines deep learning, especially convolutional neural networks (CNNs), with techniques like object detection, image segmentation, and feature extraction. In the real world it powers face recognition, medical imaging diagnosis, self-driving perception, manufacturing defect detection, and retail analytics. Because visual data is used across industries from healthcare to agritech, computer vision remains one of the most in-demand AI specialisations.

To become a computer vision engineer, start with strong Python programming and the maths behind ML — linear algebra, probability, and statistics. Next, learn machine learning and deep learning fundamentals, then specialise in CNNs, OpenCV, and frameworks like PyTorch or TensorFlow. Build a GitHub portfolio with end-to-end projects such as object detection, image segmentation, or OCR, and deploy at least one of them so you understand the full pipeline from data collection to monitoring. Entry routes include ML internships, AI startup roles, or transitioning from software or data roles, and adding modern skills like Generative AI and vector databases makes your profile far stronger.

Computer vision engineer salary in India depends heavily on experience, city, and company type. Freshers typically start around ₹4–8 LPA, engineers with 3–6 years of experience commonly earn ₹12–25 LPA, and senior or specialised roles at product companies and funded AI startups can go well beyond ₹30 LPA. Compensation rises faster for engineers who can own the complete pipeline — model development, deployment, and monitoring — or who combine vision skills with LLMs and Generative AI.

Common computer vision interview questions cover CNN fundamentals (convolutions, pooling, receptive fields), transfer learning, object detection models like YOLO and Faster R-CNN, image preprocessing, handling imbalanced datasets, overfitting, and evaluation metrics such as IoU and mAP. Interviewers also deep-dive into your projects — why you chose an architecture, how you tuned it, and how you deployed it. Practise explaining one end-to-end project clearly, including trade-offs, since that discussion usually carries the most weight in the interview.

Start with a computer vision course that teaches Python and OpenCV basics before moving into deep learning and CNNs. Pick one that is project-based, regularly updated, and covers modern architectures like vision transformers rather than only classical methods. Free university lectures combined with guided hands-on projects work well for most beginners — just remember that in hiring decisions, a GitHub portfolio of real projects matters more than the course certificate itself.

Computer vision jobs in India include titles like computer vision engineer, machine learning engineer (vision), applied scientist, and research engineer. Hiring is strong in automotive and drones, medical imaging and healthtech, manufacturing quality inspection, surveillance and security, retail analytics, agritech, and AR/VR. Most opportunities are concentrated in Bengaluru, Hyderabad, Pune, and Gurugram, across AI startups, global capability centres, and large product companies, and startups often let you work across the full data science pipeline rather than one narrow task.

Generative AI is the branch of AI that creates new content — text, images, code, audio, or video — by learning patterns from massive datasets. Most modern systems are built on transformer-based large language models (LLMs) for text and diffusion models for images: they take a prompt and generate output step by step, then are fine-tuned or grounded with techniques like RAG for specific use cases. This is why tools like ChatGPT, Gemini, and image generators can produce human-quality results from simple instructions.

Generative AI vs agentic AI comes down to what the system does with your input. Generative AI creates content — it answers, writes, codes, or generates images when prompted. Agentic AI goes a step further: it can plan multi-step tasks, use tools like search or code execution, and act toward a goal with minimal supervision, usually built on top of generative models. For example, a chatbot drafting an email is generative AI, while an agent that researches the topic, drafts the email, schedules it, and follows up is agentic AI.

Begin with an LLM assistant such as ChatGPT, Claude, or Gemini and learn prompt engineering, since that foundation applies everywhere. Then pick up Python-based generative AI tools and frameworks like Hugging Face, LangChain, or LlamaIndex, understand how vector databases power retrieval-augmented generation (RAG), and explore image generation with tools like Stable Diffusion. Focus on the underlying concepts — embeddings, fine-tuning, RAG — because specific generative AI tools change quickly, but these fundamentals transfer to every new tool.

A free generative AI course with certificate is a smart, low-risk way to learn the fundamentals and add a credential to your resume, especially if you are a student or switching from another domain. However, certificates alone rarely get you hired — recruiters look for proof that you can build. Use the free course to grasp LLMs, prompt engineering, and RAG, then immediately build two or three hands-on projects and host them on GitHub; that combination is what moves your resume to the interview pile.

A vector database in RAG (retrieval-augmented generation) is the store that holds the embeddings of your documents. When a user asks a question, the query is converted into a vector, the database quickly retrieves the most similar chunks, and those chunks are passed to the LLM so the answer is grounded in your actual data instead of the model's memory alone. This is what allows RAG systems to answer questions over private or company-specific knowledge while sharply reducing hallucinations.

Vector databases work by converting data — text, images, or audio — into high-dimensional numerical vectors called embeddings, where similar items end up close together in that vector space. When a query arrives, it is embedded the same way, and the database uses approximate nearest neighbour (ANN) algorithms such as HNSW or IVF to find the closest stored vectors in milliseconds, even across millions of records. Most also support metadata filtering and hybrid search, which is why they have become the backbone of RAG and semantic search applications.

The best way to learn vector databases is to start with embeddings — understand how text or images become vectors and how cosine similarity measures closeness. Then set up a beginner-friendly option like Chroma or Qdrant locally (or pgvector if you already know SQL) and build a small RAG app that answers questions over your own documents. Once that works, go deeper into ANN indexing, metadata filtering, and scaling — learning them alongside a real LLM project makes the concepts stick far better than theory alone.

The most widely used open source vector databases are Milvus, Qdrant, Weaviate, and Chroma, along with pgvector, which adds vector search directly to PostgreSQL, and FAISS, which is a library rather than a full database. If you are prototyping, Chroma or pgvector are the quickest to set up; for large-scale production workloads, Milvus and Qdrant are strong choices. Pick based on your scale, hosting preference, and the stack your team already uses.

Vector databases interview questions usually begin with fundamentals — what embeddings are, how cosine similarity differs from dot product, and why approximate nearest neighbour search (HNSW, IVF) is needed at scale. Expect scenario questions on designing a RAG pipeline, choosing between vector database options, handling metadata filtering and hybrid search, and tuning the trade-off between latency and accuracy. Prepare a clear story about a project where you actually used one, including the trade-offs you made, because that is where most interviewers dig deeper.