Data Engineering
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- Expertise in building unbiased datasets via feature-based sampling.
- Proficient in generating synthetic data matching real data distribution.
- Skilled in augmentation techniques for vision, speech, NLP.
Modelling and Feature Engineering
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- Comprehensive knowledge of Convolutional, Recurrent, and Transformer-based models.
- Experience with feature importance techniques.
- Proficient with Contrastive Learning methods like SimCLR, BYOL, SimSiam.
- Well-versed in model debugging and profiling.
- Experienced with topic models using Probabilistic Graphic Models and embedding-based clustering.
Training
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- Proficient in distributed training using OpenMPI + RoCE, Torch RPC.
- Skilled in Pytorch Lightning optimizations.
Calibration
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- Expertise in implicit calibration techniques like Focal Loss, Maximum Entropy Regularization, Label Smoothing, Random Dropout.
- Experience with explicit calibration techniques like Isotonic Regression, Platt’s scaling.
Optimizations
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- Skilled in model optimizations such as Quantization, Pruning, Distillation.
- Proficient in ML Ops Fusing techniques like ONNX, TorchDynamo, TVM.
Inference
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- Expertise in C++ inference using ONNX & Drogon.
- Experience with frameworks like Triton, Mosec.
- Skilled in scaling on k8 using OKD.
- Proficient in monitoring and alerting using Vector.io, Prometheus, Grafana.
Online Monitoring
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- Expertise in hard negative mining around calibrated threshold region.
- Experience with sampling and saving hard negatives.
- Skilled in detecting and alerting on Model and Data Drifts.