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N #2200
Applications open Full-Time Job

Forward Deployed AI / Software Engineer.

Talendata Technologies

  • Remote, India
  • 8-9.5 Lacs PA
  • 7-8 Yrs
  • Posted Oct 24, 2026
HR
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About the role

Location: Remote Experience: 7 Years Budget: Up to 80,000 Working Hours: 5 PM โ€“ 1 AM IST (Flexible)

Eligibility & process

Who can apply

Experience: 7-8 Yrs. Open to matching qualifications.

Skills & stack

Artificial Intelligence Python Deployment TypeScript Cloud Production Machine Learning PyTorch

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 (Artificial Intelligence, Python, Deployment, TypeScript, Cloud, Production, Machine Learning, PyTorch) & 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 Talendata Technologies 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 Talendata Technologies as a Forward Deployed AI / Software Engineer.?
Highlight Talendata Technologies's market reputation, recent tech innovations, and how your skills in Artificial Intelligence 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.
Talendata Technologies 8-9.5 Lacs PA
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