My Mock Interview for Machine Learning Engineer (2025). As part of my preparation for AI/ML/DL engineer roles, I designed a 30-minute mock interview that covers behavioral, technical, case studies, databases, and system design questions. This interview-style blog not only reflects my journey but also demonstrates the kinds of answers I’d give in a real interview setting.
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I’m Mirza Yasir Abdullah Baig, a Computer Science graduate from the University of Lahore (2023). I started my career in web development and worked as a WordPress developer for 7 months. During that time, I realized AI would soon dominate the tech landscape and decided to fully dedicate myself to Machine Learning.
In Feb 2025, I began learning Python, OOP, and libraries, then progressed to math, DSA, ML, and DL. I’ve completed multiple internships, worked on ML projects, participated in hackathons, and built a strong portfolio. In just 7–8 months, I’ve developed the skills to contribute as an ML Engineer and I’m excited to bring that energy into a full-time role.
My career goal is not just to earn a salary but to grow into one of the best AI/ML engineers. I believe your company provides the right environment — strong AI-focused projects, talented teams, and room to learn and innovate. I am hardworking, adaptable under pressure, and passionate about problem-solving. This role aligns perfectly with my long-term growth.
One of my proudest projects was building a recommendation system. Initially, deployment was a challenge because the model file was too large. After extensive research and experimenting with APIs, I solved it by hosting the file via a URL and successfully integrated it with the system. This taught me persistence and creative problem-solving in real-world deployments.
I always respect my team and manager, knowing they have valuable experience. But I also believe in ownership and sharing opinions. If I disagree, I explain my reasoning respectfully and ask for feedback on why my approach may not fit. This way, I show confidence without ego, and it helps me improve continuously.
In one deployment, my API integration failed repeatedly and caused frustration. Instead of continuing blindly, I stepped back, reassessed, and deployed the model on Streamlit instead of HuggingFace. That failure taught me the importance of flexibility and exploring alternate solutions quickly.
I aim to deliver ahead of time. For example, if a deadline is 3 days, I target finishing in 2 days. This gives me an extra buffer to test, improve, and polish the project while still being on time.
For example, overfitting: Imagine a student memorizes answers to a specific exam instead of truly learning the subject. They ace that exam but fail in real life. Similarly, an overfit model performs well on training data but poorly on new data.
I dedicate daily time to reading papers, following AI communities, and learning from platforms like Coursera. My curiosity ensures I keep pace with the fast evolution of AI.
AI is transforming the world, from entrepreneurs to global leaders. I see AI as a tool to solve big problems and create opportunities. As a CS graduate, I want to be part of this change.
I see myself at a managerial or research level, possibly moving into robotics or advanced AI research, while continuing to build impactful solutions.
Regularization adds a penalty to model weights to prevent overfitting.
L1 (Lasso): pushes some weights to zero → feature selection.
L2 (Ridge): shrinks weights smoothly → keeps all features but smaller.
Often combined as ElasticNet.
Classification: predict discrete labels (spam vs. not spam, disease vs. healthy).
Regression: predict continuous values (house price, temperature).
Both use supervised learning, but differ in output type and loss functions.
Detect: Training accuracy >> Validation/Test accuracy. Loss diverges.
Handle: Add regularization (L1/L2), use dropout, gather more data, apply data augmentation, or use early stopping.
Cross-validation splits data into k folds. The model is trained on k-1 folds and tested on the remaining fold. This repeats for all folds, and average performance is taken. It ensures stable, unbiased performance estimates and prevents over-reliance on a single train/test split.
Backpropagation calculates the loss, then propagates errors backward through layers using the chain rule of calculus. Gradients are computed for each parameter, and weights are updated with optimization methods like gradient descent.
CNNs: extract spatial features → best for images/vision tasks.
RNNs: capture sequential dependencies → good for time series and text.
Transformers: use attention → handle long sequences, parallelized, state-of-the-art in NLP and vision.
Batch normalization standardizes activations within each mini-batch.
It stabilizes training, reduces internal covariate shift, speeds convergence, and allows higher learning rates.
Attention assigns importance scores to different parts of the input, so the model focuses on the most relevant information. In NLP, this lets models look at relevant words in a sentence, enabling Transformers to outperform RNNs.
Dropout randomly deactivates neurons during training. This prevents co-dependency among neurons, forces the network to learn robust representations, and reduces overfitting.
SQL databases are relational, structured, and good for transactions (e.g., MySQL, PostgreSQL). NoSQL databases are flexible and scale horizontally, often used for large unstructured data like JSON or key-value stores (e.g., MongoDB, Cassandra).
An index is like a book’s table of contents. It allows the database to quickly locate data without scanning the whole table. This speeds up read queries but may slow down writes.
JOINs combine data from multiple tables. For example, INNER JOIN returns only matching rows, LEFT JOIN keeps all rows from the left table, and so on. They’re essential for relational database queries.
Sharding means splitting large datasets across multiple servers (horizontal partitioning). Each shard holds a subset of data, which helps handle big workloads and improves scalability.
A typical schema has three tables: Users, Items, and Interactions (like ratings, clicks, or purchases). This setup supports collaborative filtering and content-based recommendations.
First, collect email data and label as spam or not. Preprocess by removing stopwords, tokenizing, and converting text into features (e.g., TF-IDF). Train a classifier like logistic regression or Naive Bayes. Evaluate using F1 score since data may be imbalanced. Finally, deploy with an API for real-time classification.
Fraud detection needs real-time analysis. I’d use streaming data pipelines, preprocess transactions, and apply anomaly detection or classification models. A challenge is extreme imbalance (fraud is rare), so I’d use class weights or anomaly detection. Alerts must be fast and accurate to prevent losses.
New users or items don’t have interaction history. To solve this, we can use content-based filtering (recommend based on features like genre, price, etc.), or a hybrid approach combining collaborative filtering once enough data is collected.
If accuracy drops in production, I’d first check if training and production data distributions differ (data drift). Then I’d verify feature pipelines for leakage or missing preprocessing. If needed, retrain with updated data and improve monitoring.
Collect customer behavior and demographics, preprocess features, then train a classification model like logistic regression or random forest. Evaluate with recall/F1 score since missing churners is costly. Finally, integrate results into CRM for retention strategies.
To serve millions of requests, I’d containerize the model, use load balancers, and deploy with TensorFlow Serving or TorchServe. Caching frequent results and scaling with Kubernetes ensures low latency and reliability.
I’d log features and predictions in production, compare their distributions with training data using statistical tests (KL divergence, PSI). If drift is detected, trigger retraining pipelines.
Use distributed training (e.g., PyTorch DDP), data sharding, and a feature store. For serving, use distributed storage and horizontally scale inference servers.
CI/CD automates model training, testing, and deployment. With tools like Airflow, Kubeflow, or GitHub Actions, each code or data update can retrain models, test them, and push to production with minimal manual effort.
The ML model is wrapped in an API (REST/GraphQL). It interacts with a database to fetch input features and store predictions. Microservices architecture helps connect everything in a scalable and modular way.
