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N #3154
Applications open Full-Time Job

Generative AI ML Engineer

Varite

  • India (Remote / Multiple), India
  • 15-25 Lacs PA
  • 8-12 Yrs
  • Posted Today
HR
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About the role

Hands on experience on Model deployment and Model Monitoring on any public cloud (AWS / Azure/ GCP) Experience in designing & implementing solution in mentioned areas: Strong experience in developing AI / ML models on Google Cloud Platform Core Skills : TensorFlow,Recommendation Systems,Deep Learning,NumPy,Pandas,Sklearn,Python,R,Keras,Statistical Analysis,Computer Vision,PySpark. .

Eligibility & process

Who can apply

Experience: 8-12 Yrs. Open to matching qualifications.

Skills & stack

Data Science Google Cloud Platforms Keras Artificial Intelligence Google PyTorch Cloud Platform Data

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Data Science, Google Cloud Platforms, Keras, Artificial Intelligence, Google, PyTorch, Cloud Platform, Data) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Varite Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Varite as a Generative AI ML Engineer?
Highlight Varite's market reputation, recent tech innovations, and how your skills in Data Science directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Varite 15-25 Lacs PA
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