Skip to main content

Applied AI Mock Interview: NumPy, pandas & RAG

A 90-minute live notebook mock interview with technical follow-up questions and practical feedback.

Majid M.
By Majid M.5.0· 4 reviewsAI/ML mocks | NVIDIA DLI Instructor | Stanford, JHU & Penn researchers

Practice the applied AI notebook interview with me playing the interviewer. You'll work in your own practice notebook while I ask technical follow-up questions and press you to explain your code and decisions. One 90-minute live session — $225 total, one-time. Session plan • 60 minutes: a timed mock interview using your dataset, starter notebook, and interviewer script. • 30 minutes: feedback on code, reasoning, communication, and what to practice before your interview. We'll focus on pandas and NumPy, batched embedding calls, vectorized cosine top-k retrieval, and precision, recall, NDCG, and hit rate. Follow-up questions will cover retrieval failures and tradeoffs in metadata filters, hybrid search, and chunking. We'll prioritize the core exercise and discuss extensions as time allows. Before we meet, please send your practice dataset, starter notebook, interviewer script, expected interview length, and any areas you want me to press on. I'll review the materials ahead of the session. To get started, purchase this offering and send me a few times that work before your interview. We'll confirm a time in Leland messages. You'll leave with specific feedback and clear preparation priorities.

What's included

1h 30m of coaching$150/hr · Live Sessions$225
Total$225

Meet your expert

Majid M.

Majid M.

AI/ML mocks | NVIDIA DLI Instructor | Stanford, JHU & Penn researchers

5.04 reviews
345Min coached
5Followers

I help candidates prepare for AI and ML roles with technical, behavioral, and hiring-manager mock interviews. Technical sessions cover Python, NumPy and pandas, ML fundamentals, RAG and retrieval evaluation, agent tool calling and evaluation, and AI system design. For behavioral and hiring-manager rounds, we'll practice role fit, project deep dives, impact, teamwork, and stakeholder communication using examples from your real experience. I've worked in AI since 2015 and am an NVIDIA DLI Certified Instructor for Building Agentic AI Applications with LLMs and a University Ambassador. I have a Ph.D. in Computer Science and experience as a Data Scientist at the University of Pennsylvania. My AI research has included joint collaborations with researchers from Stanford, Johns Hopkins, and the University of Pennsylvania, including multimodal machine learning for human wellbeing and audio-visual prediction and explanation. At Utah Valley University, I teach applied AI and develop learning resources on LLMs, generative AI, model evaluation, and responsible AI. I lead technical work on DataGovAI, a multi-agent RAG project for government records-retention compliance with the Utah Office of Data Privacy. I have also delivered NVIDIA DLI workshops at UVU and the University of Utah. Selected app case studies • AI-ClassSims: Co-PI and technical contributor on multi-agent classroom simulation for teacher preparation. • CareWise: Developer and technical lead on a citation-grounded caregiver-information research prototype. • Willow: Built a no-login demo and chat-stability fixes for a streamed LLM reflection-companion prototype. Case studies and public code samples: https://github.com/memari-majid#web-app-case-studies The case-study page includes the checked demo availability for each app. Certifications and professional training • NVIDIA DLI Certified Instructor — NVIDIA, certified November 17, 2025, for Building Agentic AI Applications with LLMs. • AI For Business Specialization — University of Pennsylvania, through Coursera; completed September 5, 2026. • Hands-on quantum error correction with Google Quantum AI — Google Quantum AI, through Coursera; completed October 28, 2026. • Generative AI with Large Language Models — DeepLearning.AI, through Coursera; completed October 16, 2024. • Practical Data Science on the AWS Cloud — DeepLearning.AI, through Coursera; issued October 2022. • Build Better Generative Adversarial Networks (GANs) — DeepLearning.AI, through Coursera; completed November 4, 2021. • Custom Models, Layers, and Loss Functions with TensorFlow — DeepLearning.AI, through Coursera; completed July 30, 2021. • Previously held Registered Product Owner™ — Agile Education by Scrum Inc.™; issued December 2024 and expired December 2025. Credential details and verification links: https://www.linkedin.com/in/majid-memari/details/certifications/ Bring your target job description and a practice notebook, API schema, agent trace, or system design prompt. We'll choose the interview round you want to practice, work through follow-up questions, and identify what to improve. Message me with your interview date, format, current experience, and the areas you want to strengthen.

View Majid's full profile

More offerings from Majid