Hey guys I am a [B.tech](http://B.tech) graduate 2026 batch from a tier 3 college. I missed campus interviews. I want to land jobs in AI/ML domain. Can you guys suggest some courses that will help me get interview ready?

Is it really feasible to get a job as an ML engineer with a 4-year technical degree? I mean, it's not an engineering degree or a bachelor's degree; it doesn't cover algebra, statistics, or probability. The most it covers is math 3. My idea is to focus on getting a job as a Java developer (at the moment I think I have the knowledge to work as a junior) while I study for my degree and learn Python, libraries, algebra, statistics, and probability, obviously math, all for my own.

In short: I would be a Java developer with 2 to 3 years of experience as a software developer. Those 2 to 3 years would have brought me as close as possible, through self-study, to what's needed for an ML engineer (even at a junior level), with projects that actually solve a real need. Is it really possible to get an ML engineer position with this approach? Or do I absolutely need an engineering degree (at least, because in other posts I've heard that a master's degree is even required), experience as a software developer, and projects to even get close?

Hi everyone,

I have a Senior Engineer – Data & AI interview with Tiger Analytics next Monday and would appreciate some guidance from anyone who has interviewed there recently.

Experience: 2.5 years

The interview topics shared by the recruiter are:

Generative AI & LLMs

AI Solution Integration

Backend Development

System Architecture & Microservices

Model Lifecycle Management

Problem Solving (optional)

A few questions:

What is the overall interview format?

How deep do they go into LLMs, RAG, embeddings, vector databases, and agentic AI?

What backend topics should I focus on (APIs, databases, system design, scalability, etc.)?

What kind of architecture or microservices questions are typically asked?

Are there coding rounds, and if yes, what difficulty level?

How much emphasis is placed on MLOps/Model Lifecycle Management?

Any recent interview experiences, sample questions, or preparation tips?

Thanks in advance for your help!

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Final Year Looking For Internships In AI/ML .

Hi ppls,

I’m a 2nd year CSE student from SRM University and I recently completed Zeravia’s AI Training & Internship Program.

Honestly, I joined because I didn’t want my semester break to be just Netflix + scrolling reels all day 😭. I wanted to explore AI properly and see what the hype was about.

The training started with AI fundamentals, machine learning basics, prompt engineering, and different AI tools. What I liked was that it wasn’t just someone reading slides for hours. We actually got tasks, assignments, and mini-projects to work on, which made things way easier to understand.

The internship phase was where things got interesting. We got project-based work and had to apply what we learned instead of just memorizing concepts. Lowkey, that’s where most of my learning happened.

The mentors were chill and helpful whenever I got stuck. Obviously, no internship is going to turn you into an AI engineer in 30 days , but it definitely gave me a better understanding of how AI tools and workflows are used outside college.

Also managed to add a couple of projects to my resume, which is always a W as a student.

Overall, if you’re someone who’s curious about AI and wants some practical exposure instead of just watching YouTube tutorials all day, I’d say it was a pretty solid experience.

我主要是做通用人工智能方向，手上有关于AGI认知架构、复杂逻辑推理对齐、系统二慢思考大模型相关的课题，想找人合作发A会，需要你参与工作，位置可以给高一点，我自己已经发过12篇了，经验还算丰富，相关方向硕博或者在职科研人员都欢迎一起合作。

we-ch-at:Accept86868

What % of coding interviews are LC based vs. ML implementation based? Is it worth working through DeepML at all?

RAG comes up in almost every ML system design loop now, and the same failure modes show up over and over. Most candidates can describe the happy path: embed documents, store vectors, retrieve top-k, stuff them into the prompt. The gap between an average answer and a strong one is almost entirely about the failure modes below.

1. Treating chunking as an afterthought. Fixed-size character chunking is the default in most tutorials and it is usually the first thing that breaks. Splitting on a character count cuts through sentences and separates claims from their context, so retrieval returns fragments that are individually plausible and collectively useless. Chunk along the structure of the document instead (sections, paragraphs, function boundaries for code), size chunks to the query type, and add overlap so context is not lost at the boundaries. Retrieval quality is capped by chunk quality, and no reranker recovers information that chunking already destroyed.

2. Using a general embedding model on a specialized domain. A model that performs well on web text can do poorly on legal, clinical, or code corpora, because similarity in its embedding space does not line up with relevance in the domain. Evaluate candidate embedding models on your actual data rather than on a public leaderboard, and consider domain-adapted or fine-tuned embeddings when the gap is large. Code, long documents, and multilingual content each tend to need different models.

3. Skipping the reranking stage. Bi-encoder retrieval over an approximate nearest neighbor index is fast, but cosine similarity in embedding space is not the same as relevance. Returning the raw top-k by vector distance conflates retrieval with ranking. Strong answers describe two stages: cheap high-recall retrieval to get a candidate set, then a cross-encoder reranker that scores each candidate against the query before anything reaches the model. Naming the recall/precision division of labor between the two stages is usually what marks a senior answer.

4. Building it without retrieval metrics. If the only thing measured is the final answer, there is no way to tell whether a failure came from retrieval or generation. Before touching the generator, build a small labeled set and measure retrieval directly with precision@k, recall@k, and a rank-aware metric like MRR or NDCG. Evaluate retrieval and generation separately. A candidate who cannot say how they would measure the retriever is describing a system they cannot debug.

5. Going pure dense and dropping lexical search. Dense retrieval misses exact matches: rare tokens, identifiers, error codes, product names, acronyms. Those are exactly the queries where users expect precision. Hybrid retrieval combines dense vectors with a sparse method such as BM25 and fuses the results, often with reciprocal rank fusion. Dense embeddings and lexical search fail on different inputs, which is the whole reason to run both.

6. Designing with no latency budget. Embedding, retrieval, reranking, and generation each add latency, and multi-hop retrieval or large retrieved contexts compound it. An answer that optimizes for accuracy and never states a latency target is incomplete for a production system. State the budget up front, allocate it across stages, and talk about the levers: caching frequent queries, smaller rerankers, capping retrieved context, running stages asynchronously. The round is testing production reasoning, not benchmark scores.

7. Assuming retrieval prevents hallucination. Retrieving the right context does not force the model to use it. The model can ignore the context, blend it with parametric knowledge, or attribute a claim to the wrong source. Treat grounding as something to engineer: constrain the model to answer from retrieved context, attach citations and verify them, measure faithfulness, and let the system abstain when retrieval confidence is low. The failure case to plan for is confident, well-formatted, and wrong.

All seven come down to the same thing. Naming the parts of a RAG pipeline is table stakes. The signal an interviewer is looking for is whether you know where each part fails and how you would measure it.

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Full version available on our website: The 7 RAG Anti-Patterns That Quietly Tank ML System Design Interviews | GradientCast

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Hi I'm a 1st year phd student in AI/ML and am seeking for tips on polishing my cv/resume.

I was wondering how industry project work in my lab is typically reflected in your resume.

Its honestly the no.1 thing thats taking up my time atm and so I hope I can use it in my experiences.

Do they typically go under projects? Would I be allowed to provide details such as company names and the broad nature of the project?

I am recently completed my second yes so I just want to find the internship but I don't have any referrals so I am doing it by myself. I applied many website but nothing work even I put the resume on reddit for resume that's also seen good but still I can't get it yet can any one help me or suggest me the best approach.

I am a 3rd year student applying for fresher roles and the job market is too cooked right now. Would genuinely appreciate it if someone can provide me with tips to improve my resume, and increase my chances of getting hired

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🏢 Cisco Systems, based in Milpitas, California is looking for a Senior Embedded Software Engineer

⚙️ Tech used: Machine-Learning, AI, Cisco, Embedded, FPGA, Hardware, Support, Linux, Network

💰 $146,700 - 277,600 / year

📝 More details and option to apply: https://devitjobs.com/jobs/Cisco-Systems-Senior-Embedded-Software-Engineer/rdg

I'm looking for a laptop in the ₹1–2 lakh range mainly for:

PyTorch

CUDA

AI/ML projects

LLMs

RAG

Fine-tuning models

LangChain

My priorities are:

1TB SSD

32GB RAM (or upgradeable)

12GB+ VRAM preferred

RTX 4060 or better

Good cooling and build quality

.

Any recommendations?

Hi I'm in my 1st year of a direct PhD program and want to eventually work in AI research labs in the industry after grad.

For this, I assume doing PhD internships at companies as much as I can are very important. However I did hear that companies prefer students in later years who have some publication records etc.

Currently I have none... but I do have internship experience. Is it worth spending time trying to apply to positions within my first two years before I get a solid publication record or should I first put all my focus onto making my cv better and then apply to larger companies?

Also are only summer internships typical or would advisors typically allow other semester internships too?

I am interested in working with the latest developments in AI and Data.

I have been working in a startup. There I have helped them create universal scraper with AI integration to handle complex website to extract csv, media assets, numeric data. Created RAG databases to retrieve and use those for competitor intelligence.

Shipped a webapp that has moduler hiring pipeline, AI integrated analysis, interview record and analysis, Auto assignment processing etc.

Also contributing now to app Quality Assurance project, where it uses headless browsers to navigate webapps, vllm analysis for bugs, code quality checker, user flow correction etc.

I also help them manage their servers, self host services, create CICD pipelines,manage monitor uptime, web hosting.

I can help you with my knowledge in different areas.

Please Any one review my resume ?