---
title: "Prateek S. - AI Architect and Researcher in Greater Bengaluru Area | Tal"
description: "Meet Prateek S., AI Architect | Enterprise ML Systems | Driving Strategy Through Data | IIT Kharagpur | US Patents based in Greater Bengaluru Area. AI stack includes claude code, hugging face, and langgraph. Explore public work links including github and medium."
image: "https://tal.club/meet/prateek-kumar-sikdar/share-image"
---

# Prateek S\. on Tal

## Headline

AI Architect \| Enterprise ML Systems \| Driving Strategy Through Data \| IIT Kharagpur \| US Patents

## About

AI Architect with 14\+ years of experience building scalable machine learning systems across banking and enterprise technology\. My work spans experimentation at scale, intelligent systems, and AI\-driven optimization\. I am deeply interested in how strategy, data, and emerging technologies reshape competitive advantage — and how leaders can harness them responsibly for long\-term value creation\.

## Location

Greater Bengaluru Area

## AI stack

claude code

hugging face

langgraph

Qwen

## Work experience

AI Architect and Researcher at Accenture, Mar 2026 \- Present · 7 mo, Bangalore, Published CHIAR\-Former \(arXiv:2606\.08327\): novel \~400M\-parameter efficient transformer routing tokens between DCT spectral mixing and full self\-attention via per\-token spectral entropy; WikiText\-103 Test PPL 23\.58 at 37% FLOP reduction\. Published LayerRoute \(arXiv:2606\.01838\): frozen Qwen2\.5\-0\.5B backbone with per\-layer hard\-gated skip connections; 12\.91% FLOP skip differential \(tool calls 15\.25% vs planning 2\.34%\), trained in 6\.4 min on A100 with 0\.22% trainable params\. Conducting Target\-Space Distillation \(TSD\) experiments on TinyLlama\-1\.1B comparing TSL vs SFT students; TSL Val PPL 5\.44–7\.68 vs SFT 5\.99–10\.09 \(SFT overfits from epoch 2\)\.

Machine Learning Researcher at HP Inc\. \(R&D Center\), May 2019 \- Jan 2026 · 6 yrs 9 mos, Bengaluru, Karnataka, India, Built Transformer\-based models for time\-series anomaly detection and throughput prediction across printer manufacturing telemetry — boosted system throughput 30% via learned sequence representations\. Led VAE \(Variational Autoencoder\) anomaly detection research across enterprise manufacturing datasets, achieving defect rate under 2% — directly applicable to ad\-quality and fraud detection pipelines\. Deployed adaptive A/B testing framework using contextual multi\-armed bandit algorithms \(ε\-greedy, Thompson Sampling\) to dynamically route inference across 15\+ model variants, improving accuracy 8% — analogous to ad\-serving model selection\. Fine\-tuned BERT/RoBERTa/DistilBERT for multi\-class document relevancy classification and content scoring, achieving 94% accuracy \(F1\-optimised\) on 24K\+ image/document datasets across 15 web categories — directly transferable to ad content classification and brand\-safety scoring\. Built XGBoost classification pipelines with chronological splits and SMOTE for imbalanced data; strong out\-of\-time performance on tabular prediction tasks comparable to CTR/CVR modelling requirements\. Architected Agentic RAG system using LangGraph and multi\-agent orchestration for printer support automation, reducing customer response time 60%; evaluated via LangSmith on golden query dataset\. Built end\-to\-end MLOps pipelines \(MLflow, Docker\) covering model deployment, real\-time monitoring, and champion\-challenger A/B frameworks; contributed to 3 U\.S\. patent filings in deep learning for predictive maintenance\.

Data Scientist at Citi, Aug 2017 \- May 2019 · 1 yr 10 mos, Bangalore

Data Science Manager at Citi, Aug 2017 \- Apr 2019 · 1 yr 9 mos, Built deep learning targeting models \(TensorFlow, PySpark\) for credit cards; achieved 22% response rate improvement through customer segmentation and propensity modelling\. Built XGBoost response models saving $1M\+ in targeting costs through automated customer selection\.

Data Science Manager at Equifax, Jun 2016 \- Jul 2017 · 1 yr 2 mos, Managed credit risk scorecards \(logistic regression, gradient boosting\) improving KS by 12 points; built e\-commerce seller scorecards \(Random Forest, 72% accuracy\)\.

Data Scientist at Equifax India, Jun 2016 \- Jul 2017 · 1 yr 2 mos, Machine Learning projects

## Education

B\.Tech \(Honors\), Indian Institute of Technology, Kharagpur

I\.C\.S\.E\., St\. Vincent's Convent School

## Public links

- github: https://github\.com/hackie123
- medium: https://medium\.com/@prateeksikdar

[View the original profile](https://tal.club/meet/prateek-kumar-sikdar)

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