In a recent round of interviews at Superteams.ai, we spoke with candidates who spent 2-3 years of their early career in universities in the UK, Australia, Canada. One common fact stood out – while the academic credentials and certifications looked great on paper, they had never actually built any real-world application end-to-end. Most of them had invested tens of thousands of dollars in tuition fees and living expenses, lured by the promise of a career in AI/ML. But when they graduated, they hit a wall. 

The skills taught in these universities had very little relevance to what’s actually needed in the real world. They knew how to build demo models in Jupyter notebooks, but had never set up a cloud deployment, handled production data, or worked with the kind of messy, fast-changing problems you face when building AI for real businesses. Many had never SSH-ed into a Linux server, a tell-tale sign that they hadn’t ever worked on production systems before. Several of them could not tell the difference between memory architectures of CPUs vs GPUs, or what happens when your data doesn’t fit in the GPU memory.  

That gap between academic training and practical ability is wider than anyone admits, and it’s costing both candidates and companies valuable time and money.

Of course, it's big business for the course providers. Meanwhile, with tightening immigration rules, these students are left in a tough spot: if they don’t land a real job within their visa timelines, they’re forced to pack up and leave. The harsh truth? For all the money and effort spent, very few are coming out industry-ready, and it’s a problem the entire ecosystem needs to address.

We recently wrote about this problem in a LinkedIn post - and thought it might be useful to break it down further, and explain to candidates or students what they should learn instead. To do this, you don’t need to pay for an expensive course, and you can learn every bit of this on your own if you invest time and effort. 

Understanding GPU and System Architecture

If you want to build anything remotely production-grade in AI today, you must get comfortable with how systems work under the hood. That starts with understanding the basics of CPU vs GPU architecture: why GPUs matter for deep learning, how their memory is structured, and what the real-world constraints look like (hint: it’s not just about having a bigger card). 

When using CPU-bound processes, study how memory mapping works, how swap space can bail you out when RAM is tight (and when it will absolutely kill your performance). Also deep-dive into what happens when your model or data doesn’t fit into GPU memory. Learn about PCIe bottlenecks, VRAM vs system RAM, and why certain workloads choke on CPU but fly on a GPU (eg. indexing large amounts of data).

In addition to this, figure out what happens when your vector data or indexes are too big to fit in memory - how to batch, shard, or use techniques like memory mapping or parallelism. These are not advanced topics; they are must-knows if you want to move beyond toy projects. In typical production environments, your data size will be at least 10-15 GB, and you can easily play with that scale of data by using VectorDB with embedding models from Hugging Face or Bedrock, along with free models from OpenRouter.

Where do you start? NVIDIA’s Deep Learning Institute has a number of self-paced courses that you can try out. Look up the Open Source Computer Science Curriculum (OSSU), especially the Computer Architecture module. 

Use Linux Instead of Windows for Development

While Windows has increasingly improved as a development environment, most production systems run on a flavor of Linux. In such systems, you won’t have access to a UI - you work remotely using SSH to debug, deploy, monitor your code. So, it is vital that you develop a deep understanding of Linux systems and become supremely proficient with Linux commands - including the ones to quickly monitor a system on the fly (top, iotop, netstat etc), move around data using rsync, tunnel a port using ssh tunneling. You should become aware of where system logs go, how to serve a FastAPI-like system using Nginx or Apache, how to build and deploy Docker images. You can look up the tutorials that Linux foundation has put up.

In addition to this, good developers naturally incorporate CI/CD (Continuous Integration/Continuous Delivery), monitoring, logging and tracking into their workflow. Learn about Ansible, Jenkins, GitHub Actions, Prometheus/Grafana/Loki and such. 

And most importantly, you need to develop a strong expertise with git. Learn about git branching, rebase, rollback, pull requests. Become comfortable with Git Flow, and learn how to work on multiple versions of your codebase by working on different branches. 

Learn Fundamentals of AWS, GCP or Azure

You can create free accounts on both AWS and GCP, launch instances, play around with security groups, load balancers, S3 object storage, AWS Lambda, Bedrock and more. The more familiar you are with the AWS ecosystem, the higher are the chances that you would be able to hit the ground running when jumping into a production environment. For a spend of less than $100, you will develop more skills than you picked up during your coursework. Try to create an application from scratch, deploy it and serve it. Incorporate authentication and authorization, so that you learn how OAuth works. 

You can also experiment with deploying LLM models using vLLM and serving them as APIs using FastAPI or Flask. You can containerize your applications with Docker, and orchestrate them using tools like Docker Compose or even Kubernetes if you want to stretch yourself. Try setting up logging and monitoring using services like AWS CloudWatch or open-source tools like Prometheus and Grafana. The key is to get hands-on, break things, fix them, and build the kind of muscle memory that no textbook or video course can give you.

Master Data Engineering and ETL

One of the biggest myths in AI is that most of the work is about building models. In reality, 80% of your time is spent on data engineering: collecting data, cleaning it, transforming it, and moving it between systems. Learn how to build ETL (Extract, Transform, Load) pipelines using Python, Airflow, or Prefect. Understand how to schedule jobs with cron, automate data pulls and pushes, and handle edge cases where data is missing or malformed. 

Play with real datasets (Kaggle is a great place to start) and try to build an end-to-end pipeline, from pulling raw data, processing it, training a model, and then deploying that model as a live API. You’ll immediately see how much of the “AI” job is actually robust, repeatable software and systems engineering.

But… What About Model Building? 

Model building is an expensive proposition that very few companies can truly afford today. If you head over to LMArena, you will see that all the top models are being released by heavily funded giants like OpenAI, Google, Meta, NVIDIA, Anthropic and others. This is because model building is expensive: a typical model training process involves data labeling, data cleaning, and then numerous runs until the model fit is accurate to the task at hand. As you can imagine, very few jobs exist for model builders currently - and they are exclusively reserved for those who have spent decades in this domain. 

However, if you must build models, then become truly comfortable with linear algebra, calculus, and MLOps. Try to train a model on “real” data that has been open sourced, serve it through vLLM, track drift, and develop intuition around the mathematical models of neural networks. 

Whatever you do, try and build an AI application from scratch on a ‘largish’ amount of data, and see if you can get it to work well-enough that you can show it around. This will give you more hands-on experience with AI/ML than you would ever get during your coursework. If you do build something like this, do apply, as we are looking for folks like you! 

Current Openings at Superteams.ai

Business Development Intern

We’re looking for an MBA-graduate Business Development Intern to support our GTM efforts. You’ll work directly with the founding team to work on lead prospecting, build relationships with frontier companies, and experiment with outbound and inbound growth strategies.

Apply link

In-Depth Guides

Applications of Agentic AI in the BFSI Sector – A Deep-Dive

This blog explains how agentic AI is redefining the BFSI sector by enabling autonomous, proactive, and continuously learning digital agents.

Building Accurate AI Assistants for the Banking Sector

Here, we dive into the technologies and business imperatives behind building highly accurate AI assistants for the banking sector.

What’s New in AI

Google Expands Gemini 2.5 Family with Flash, Pro, and Flash Lite

Google has made Gemini 2.5 Flash and Pro generally available and launched Flash Lite in preview, offering faster, more affordable AI models with improved performance.

MiniMax‑M1: First Open‑Weight, Hybrid‑Attention Reasoning Model

MiniMax launches MiniMax‑M1, an open‑source hybrid Mixture‑of‑Experts model with 1 M token context, lightning attention, CISPO training, 40 K/80 K variants with high efficiency and strong benchmark performance.

Anthropic Identifies “Agentic Misalignment” Risk in AI Models

Anthropic’s latest research shows advanced AI models from Anthropic, OpenAI, Google, Meta, xAI can autonomously choose harmful actions such as blackmail or sabotage when facing shutdown threats or goal conflicts.

Midjourney Unveils V1 Video Model to Animate Images

Midjourney has launched V1, its first image-to-video model that transforms still images into short five-second clips with motion prompts and extensions, marking the start of interactive video and 3D content.

About Superteams.ai

Superteams.ai acts as your extended R&D unit and AI team. We work with you to pinpoint high-impact use cases, rapidly prototype and deploy bespoke AI solutions, and upskill your in-house team so that they can own and scale the technology. 

Book a Strategy Call or Contact Us to get started.