Infosys Machine Learning Engineer Interview Questions

4200+ verified questions, indexed by team and level. Real questions submitted by candidates who completed Infosys loops in the last 24 months.

About the Infosys Machine Learning Engineer hiring loop

Infosys hires through three tracks — Infosys SP (System Engineer, ₹3.6 LPA), DSE (Digital Specialist Engineer, ₹6.5 LPA), and Power Programmer (₹9 LPA). HackerRank-style coding (Java / Python / C++) plus 90-min Aptitude (Quant + Logical + Verbal + Pseudocode).

ML Engineering rounds score on ML system design depth (latency, throughput, freshness, fairness trade-offs), training-infra fluency, and the bridge between model offline metrics and product KPIs. Coding is secondary to ML system thinking.

Topics covered in Infosys Machine Learning Engineer interviews

  • 01ML system design (recommendations, ranking, search, fraud, ads)
  • 02Training infrastructure (distributed training, sharding, gradient sync)
  • 03Inference at scale (batching, KV-cache, quantisation)
  • 04Feature engineering and feature stores
  • 05Model evaluation (offline metrics vs online metrics, counterfactual)
  • 06Coding (Python, PyTorch / TensorFlow internals)

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