Microsoft Engineer's AI Career Advice: Learn in 1 Hour Daily

Nandita Giri, a 32-year-old senior software engineer at Microsoft in Redmond, Washington, has successfully navigated the competitive landscape of Big Tech by prioritizing artificial intelligence skills. Her career trajectory includes significant roles at Amazon and Meta, where she observed a growing demand for AI expertise. Giri's journey began at the National Institute of Technology, Kurukshetra, in India, a school frequently scouted by tech giants. She was hired by Amazon in 2018 and moved to Seattle, where she began integrating AI into internal workflows.

Through self-directed learning, Giri developed proficiency in AI, which she utilized to automate tasks and build intelligent systems. This expertise led to her recruitment by Meta in 2022 and subsequently to Microsoft in 2023. Giri advises aspiring professionals to dedicate one hour daily to studying AI, asserting that these skills will be critical for the next decade. She views AI as a coworker rather than a threat, emphasizing that managing AI is the future of software engineering.

Giri's professional journey began with a strong foundation in problem-solving and mathematics. She studied at the National Institute of Technology, Kurukshetra in India, a recruitment hub for Amazon, Microsoft, and Google. Amazon hired her straight out of college in 2018, requiring her to pass a logical analysis test and multiple problem-solving interviews. She spent four years at Amazon, where she identified patterns suitable for automation and suggested AI-based solutions for internal workflow automation and data-driven decision support systems.

Her success at Amazon led to a recruiter from Meta contacting her via LinkedIn in 2022. While she did not initially aim to work there, the opportunity allowed her to focus on building intelligent systems with large-scale data. By 2023, she was referred internally to Microsoft based on her Meta work, specifically to work on enterprise-focused AI products like Copilot. Throughout these transitions, her scope of responsibility and compensation increased with each role change.

According to Giri, most of her AI knowledge was acquired outside of formal employment. She spent hours watching YouTube tutorials, reading blogs, and practicing coding. She started by creating small AI agents for personal tasks, such as sending outreach emails, which reduced her workload from days to under an hour. This tangible success motivated her to continue learning. She utilized platforms like LeetCode for coding practice, driven by genuine interest rather than interview preparation.

Giri notes a significant shift in the job market. She observes that demand for AI roles is skyrocketing, while traditional software engineering roles have shrunk over the last five years. She states that many of her friends not working in AI have struggled to land new offers. Consequently, she views managing AI as the future of software engineering, where engineers monitor and guide AI systems rather than performing repetitive tasks manually.

Giri recommends a structured approach for those looking to enter the AI field. She suggests dedicating just one hour a day to learning. Within six months, she promises that learners will see real progress. For those unsure where to start, she offers a curated list of resources that helped her:

• 3Blue1Brown (YouTube): Excellent for visualizing the math concepts behind neural networks.
• Fast.ai: A project-based course for building real-world models.
• Andrew Ng's Machine Learning (Coursera): A widely recommended foundational course.
• Towards Data Science (Medium): Accessible blogs covering practical topics.
• The Batch by Andrew Ng: A weekly newsletter for recent AI developments.

She also advises her younger self to focus less on perfection and more on making an impact. Taking ownership early, speaking with confidence, and prioritizing learning over titles are key to long-term growth. She believes that solving meaningful problems and maintaining resilience are the true drivers of success.

Giri has experienced distinct work cultures across the three major technology companies. She describes both Amazon and Meta as incorporating fast-paced learning environments. However, she notes that Meta's codebase is more straightforward because Facebook, Instagram, and WhatsApp are built from a single repository, allowing for quicker system understanding. In contrast, Amazon's codebase is described as huge, making the first year challenging but the learning curve worth it.

Microsoft feels different entirely to Giri. She characterizes it as more enterprise-focused, operating at a massive scale. This environment aligns with her interest in building impactful tools for productivity and business transformation. Her experience highlights that while the pace may vary, the opportunity for growth and the demand for specialized skills like AI remain consistent across these industry leaders.