Overview / CareersAI Careers in Tech
The tech industry influences virtually every sector of modern life, and AI is becoming more important to the industry by the day. AI is helping tech companies innovate rapidly, leveraging data-centric business models and making global scalability possible.
Working in AI within the tech sector can involve cloud computing, consumer electronics, cybersecurity, hardware engineering, internet services, or software development, just to name a few domains. More specifically, AI powers core products and services like autonomous systems, large-scale data analytics, natural language processing, recommendation engines, search algorithms, and speech or image recognition. These uses of AI can have profound impact on companies and consumers alike.
AI will continue to be at the heart of new tech products and services, such as AI-enhanced development environments, autonomous agents, no-code machine learning platforms, and virtual or augmented reality interfaces. It could change how we interact with technology, creating systems that anticipate user intent, personalize deeply, or even collaborate creatively with humans.
What would an AI professional contribute to the tech industry?
An AI professional in tech might contribute to any aspect of AI. Roles span across research, applied machine learning, and the operationalization and deployment of machine learning solutions, often demanding both algorithmic depth and software engineering excellence. Robotics, which has seen rapid development in recent years, is also a tech subdomain where an AI professional can work.
What are the biggest opportunities and challenges for AI in the industry?
The opportunities for specializing in AI with the tech industry are vast. Tech companies are leading AI innovation, setting benchmarks in generative AI, and creating foundational models that empower practically every other industry. The primary challenges include scalability of AI systems, development and implementation of explainable and responsible AI, real-time performance constraints, and energy consumption of large models. There’s also a strong need for responsible AI governance.
How does MSAI prepare students to lead in the tech space?
MSAI students gain a solid foundation in AI algorithms, scalable computing, and systems design. Electives in human-computer interaction and cloud computing-based AI prepare students for roles in big tech and startups. Project-based learning and the industry capstone projects help students experience and tackle real tech-sector challenges with modern AI tools and techniques.
Where have MSAI students and alumni interned or worked?
- Amazon Web Services
- Autodesk
- Esri
- Grammarly
- Lightning AI
- Microsoft
- Morph Studio
- Oracle
- Palo Alto Networks
- SAP
- SliceX AI
- Why of AI
Featured Alumni
Milan McGraw
MSAI ‘21, Head of Generative AI and Machine Learning, Amazon Web ServicesMilan McGraw had multiple advanced degrees and a strong foundation in data science, having worked at tech heavyweights such as Amazon and Groupon, when he turned to MSAI. He wanted a deep understanding of deep learning and artificial intelligence so that he could then help educate others about how to best take advantage of the budding technology.
That is exactly what he does at Amazon Web Services. Just like he tells clients that there are countless ways to apply AI and deep learning to their organization, he stresses to anyone who will listen that there are also countless job opportunities for someone interested in AI.
“A lot of people think that the only job out there for an AI person coming out of (MSAI) is a machine learning engineer," McGraw said. “That's false. There are so many different careers that AI supports. Having these core fundamental skills is going to open up tons of different doors.”
Nico Tyjeski
MSAI '19, Data Scientist, Capital OneNico Tyjeski sees AI as a tool to help streamline and automate operations. At Capital One, he uses AI to read documents, understand them, and either take action or make recommendations, bringing automation to a process that otherwise would need to be reviewed manually.
To do that, he relies on lessons learned in MSAI.
“Every time I build a model or run an experiment, I leverage a range of technical skills learned in the MSAI program,” Tyjeski said. “When collaborating with teammates, I use experience gained through the program’s capstones. When participating in technical reading groups, I apply lessons learned conducting research and attending similar groups in MSAI.”
