From Software Engineer to AI Engineer – with Janvi Kalra
From Software Engineer to AI Engineer – with Janvi KalraFrom Coda to OpenAI: How Janvi Kalra taught herself AI engineering, impressed tech leaders, and built a career at the forefront of AI—plus actionable advice for landing your own role.
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— In This EpisodeWhat does it take to land a job as an AI Engineer—and thrive in the role? In this episode of The Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team. In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field. We also discuss the following:
 TakeawaysSome of the most interesting topics discussed in the conversation were these: 1. Teaching yourself the basics is still a great way to get into AI Engineering. Janvi wanted to move onto Canva’s first AI Engineering team, but she was politely rejected at first. Instead of giving up: she started to teach herself how to build apps using LLMs. She went to hackathons, educated herself online, and built apps on the side. A few months later, she was already one of the experts at work on how to work with LLMs. So the next time Canva’s AI team was expanding, the team was thrilled to have her on! My two cents: it’s much easier to transfer internally, so if you are hoping to get into AI, see if you can do something similar to what Janvi did. 2. AI Engineering interviews are all over the place. Janvi got a broad look at the AI Engineering job market, thanks to interviewing with 46 different companies (!) There doesn’t seem to be an “AI Engineer interview” format. Instead, interviews are a mix of:
This means that the best way to prepare for these interviews is to prepare for software engineering interviews: build products using LLMs on the side, and familiarize yourself with common AI Engineering concepts. 3. Do your due diligence before joining a startup. Startups are far more risky than most other types of companies: they can grow fast, but they can fail fast as well. And yet, most engineers don’t do nearly enough due diligence before deciding to leave their current position to join a startup. Janvi tried to assess startups based on:
Janvi shares tactics on how she conducted due diligence: from using the startups’ products through digging through online forums and turning to well-researched publications like The Information. 4. AI Engineering blurs the lines between “traditional” roles. At OpenAI, the lines between data scientists, frontend engineers, backend engineers and PMs are a lot more blurred. Everyone is expected to do a bit of everything, and so engineers are becoming more “full stack.” But this is true for data scientists and product managers. Another interesting part of AI Engineering is how you need to be more comfortable throwing away your work when a new model capability renders it less useful. An interesting quote: Big Tech versus startupsHere is how Janvi summarized her mental model on the upsides of working at a large tech company versus working at a startup. The following quotes are from 12:20 in the conversation: Big Tech upsides:
Startup upsides:
The Pragmatic Engineer deepdives relevant for this episodeTimestamps(00:00) Intro (02:31) How Janvi got her internships at Google and Microsoft (03:35) How Janvi prepared for her coding interviews (07:11) Janvi’s experience interning at Google (08:59) What Janvi worked on at Microsoft (11:35) Why Janvi chose to work for a startup after college (15:00) How Janvi picked Coda (16:58) Janvi’s criteria for picking a startup now (18:20) How Janvi evaluates ‘customer obsession’ (19:12) Fast—an example of the downside of not doing due diligence (21:38) How Janvi made the jump to Coda’s AI team (25:48) What an AI Engineer does (27:30) How Janvi developed her AI Engineering skills through hackathons (30:34) Janvi’s favorite AI project at Coda: Workspace Q&A (37:40) Learnings from interviewing at 46 companies (40:44) Why Janvi decided to get experience working for a model company (43:17) Questions Janvi asks to determine growth and profitability (45:28) How Janvi got an offer at OpenAI, and an overview of the interview process (49:08) What Janvi does at OpenAI (51:01) What makes OpenAI unique (52:30) The shipping process at OpenAI (55:41) Surprising learnings from AI Engineering (57:50) How AI might impact new graduates (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same (1:07:51) Rapid fire round ReferencesWhere to find Janvi Kalra: • X: https://x.com/janvikalra_ • LinkedIn: https://www.linkedin.com/in/janvi-kalra/ • Substack:
Mentions during the episode: • Dartmouth: https://home.dartmouth.edu/ • Neet Code: Blind 75: https://neetcode.io/practice?tab=blind75 • Cracking the Coding Interview: 189 Programming Questions and Solutions: https://www.amazon.com/Cracking-Coding-Interview-Programming-Questions/dp/0984782850 • Gayle Laakmann McDowell’s website: https://www.gayle.com/ • Coda: https://coda.io/ • Inside Fast’s Rapid Collapse: https://newsletter.pragmaticengineer.com/p/the-scoop-fast • Going from 0 to 600+ users in six weeks:  • Braintrust: https://www.braintrust.dev/ • Llama 3 Hackathon Summary: https://lablab.ai/event/llama-3-ai-hackathon • AI Engineering with Chip Huyen: https://newsletter.pragmaticengineer.com/p/ai-engineering-with-chip-huyen • AI Engineering: Building Applications with Foundation Models: https://www.amazon.com/AI-Engineering-Building-Applications-Foundation/dp/1098166302/r • Andrej Karpathy’s website: https://karpathy.ai/ • Neural Networks: Zero to Hero: https://karpathy.ai/zero-to-hero.html • Shishir Mehrotra on LinkedIn: https://www.linkedin.com/in/shishirmehrotra/ • Coda Brain: https://coda.io/product/coda-brain • Vanity metrics to ignore and why hypergrowth matters: lessons from job hunting in AI – part 1: • Cursor: https://www.cursor.com/ • Windsurf: https://windsurf.com/ • Hebbia: https://www.hebbia.com/ • Motiff: https://motiff.com/ • Fireworks: https://fireworks.ai/ • Together: https://www.together.ai/ • Pinecone: https://www.pinecone.io/ • Weaviate: https://weaviate.io/ • Chroma: https://www.trychroma.com/ • Arize: https://arize.com/ • Galileo: https://www.usegalileo.ai/ • Open AI: https://openai.com/ • Yash Kumar on LinkedIn: https://www.linkedin.com/in/yash298/ • System Design Interview – An insider's guide: https://www.amazon.com/System-Design-Interview-insiders-Second/dp/B08CMF2CQF • JavaScript: https://www.javascript.com/ • Python: https://www.python.org/ • Typescript: https://www.typescriptlang.org/ • React: https://react.dev/ • Assembly: https://en.wikipedia.org/wiki/Assembly_language • Mythical Man-Month, The: Essays on Software Engineering: https://www.amazon.com/Mythical-Man-Month-Software-Engineering-Anniversary/dp/0201835959/ • Software Architecture: Perspectives on an Emerging Discipline: https://www.amazon.com/Software-Architecture-Perspectives-Emerging-Discipline/dp/0131829572 • Software architecture with Grady Booch: https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch • The Almanack of Naval Ravikant: A Guide to Wealth and Happiness: https://www.amazon.com/Almanack-Naval-Ravikant-Wealth-Happiness-ebook/dp/B08FF8MTM6 • AI Engineering in the real world: https://newsletter.pragmaticengineer.com/p/ai-engineering-in-the-real-world • The AI Engineering Stack: https://newsletter.pragmaticengineer.com/p/the-ai-engineering-stack • Building, launching, and scaling ChatGPT Images: https://newsletter.pragmaticengineer.com/p/chatgpt-images • MCP Protocol: a new AI dev tools building block: https://newsletter.pragmaticengineer.com/p/mcp — Production and marketing by Pen Name. You’re on the free list for The Pragmatic Engineer. For the full experience, become a paying subscriber. Many readers expense this newsletter within their company’s training/learning/development budget. This post is public, so feel free to share and forward it. If you enjoyed this post, you might enjoy my book, The Software Engineer's Guidebook. Here is what Tanya Reilly, senior principal engineer and author of The Staff Engineer's Path said about it:
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