SHUBHANKAR_TIWARI
SYSTEM STATUS: ONLINEColumbia MS AI '27Patent Inventor (2026)

Shubhankar
Tiwari

Software Engineer with 4+ years building enterprise microservices at Bank of America • Incoming M.S. in AI at Columbia University.

Building AI evaluation platforms, entropy-aware generation controls, and drift-aware model governance systems.

0+
CashPro Corporate Users
0M+
Enterprise Searches Served
0+ Yrs
Enterprise Backend SWE
Top 4.1%
Kaggle Expert (#2,441)
TelemetryLatency 18ms avgAUC 0.975Policy gates live
Explore AI Systems Experience & Patent CLI Terminal
Background

About & Core Focus

Shubhankar Tiwari

I've always enjoyed understanding how complex systems work. Whether I'm working on enterprise software or machine learning, I'm most interested in how these systems behave, why they fail, and how they can be made more reliable.

At Bank of America, I spent four years building, modernising, and maintaining backend services for a large-scale enterprise platform. That experience taught me the engineering trade-offs involved in designing software that evolves while remaining robust.

I apply that same systems mindset to AI. Over the past year, I've been exploring AI by building systems for LLM evaluation, model governance, and reliability engineering. Those experiences have strengthened my interest in understanding how intelligent systems learn, reason, and behave — not just how they can be evaluated.

I'm pursuing an M.S. in Artificial Intelligence at Columbia University to deepen that understanding through structured study and research.

0+ Years
Enterprise Production SWE
#2,441
Kaggle Expert / 59.7K
94.4%
B.Tech CSE GPA (SRM IST)
34
Public ML Notebooks
Engineering Philosophy

Systems Mindset Applied to Artificial Intelligence

[01]

Reliability Over Hype

Evaluating model behavior token by token. Catching failure modes during generation, not after user delivery.

[02]

Deterministic Governance

Building policy controllers, CI gates, and business-loss promotion metrics that prevent silent model degradation.

[03]

Production Rigor

4 years of enterprise backend engineering at Bank of America — 17+ releases, 13 CashPro services, 550K+ users.

[04]

Continuous Adaptation

Integrating real-time PSI & KL drift detection with shadow deployment to evaluate recovery before promotion.

Career Track

Professional Experience & Patent

Served as Subject Matter Expert (SME) for CashPro Search — the primary search platform across Bank of America's flagship digital banking ecosystem, serving 550,000+ corporate users across 40,000 companies with 18 million+ searches since launch.

Owned OpenShift deployments, CI/CD configuration, release validation, and production stabilization across 17+ release cycles. First point of contact for cross-team production escalations and architectural decisions.

Contributed to modernization initiatives including JDK 17, Spring 6, containerization, and cloud enablement across 13 CashPro services, improving team standing in vulnerability, security, and modernization metrics via Checkmarx and SonarQube scans.

Inventor on a Bank of America patent under filing (2026) for a governed multi-modal AI architecture enabling canonical truth maintenance, selective component regeneration, and audit-grade provenance in enterprise financial intelligence systems.

JavaSpring BootREST APIsOpenShiftCI/CDSQLCheckmarxSonarQube

Bank of America — Global Recognition Awards

Gold Award

Global Recognition • BofA

Backend delivery, release ownership, & cross-team lead

Silver Award

Global Recognition • BofA

Cloud enablement & OpenShift service migration

Bronze Award

Global Recognition • BofA

Production incident stabilization & triage

Systems & Models

Featured Engineering Systems

Production-grade software & AI reliability frameworks built to observe, evaluate, and govern intelligent systems.

BROWSE ALL 10 PROJECTS
Interactive Architecture Visualizer
Live Interactive Pipeline

Inference Prompt

Metric: 213 Evals

Token-level streaming input with live entropy extraction

AI Reliability Platform for LLM Evaluation
2026-PresentActive

AI Reliability Platform for LLM Evaluation

Built a full-stack system to monitor and control LLM generation at the token level, with adaptive instability detection and real-time intervention.

•Entropy-aware routing, adaptive regeneration policies, and behavioral workflows across 213 prompts spanning 5 behavioral categories
•100% harmful-output catch rate on 43 adversarial prompts across 9 holdout rounds on fine-tuned mT5; extended testing to Qwen 2.5-7B, Llama 3 8B, and Mistral v3
PythonMLOpsLLM
Drift-Aware Fraud Detection — ML Lifecycle & Model Governance
2026-PresentActive

Drift-Aware Fraud Detection — ML Lifecycle & Model Governance

Built an end-to-end ML system that detects data drift, evaluates model degradation, and governs retraining decisions.

•Complete ML system loop: Frozen Dataset → Failure-mode metrics (AUC degradation, PSI drift) → Δ Analysis → Policy Gate (Business Loss < 0) → CI Promotion
•Maintains 0.975 AUC-ROC baseline with business-loss-aware promotion gates using 10:1 FN/FP cost weighting
FastAPIscikit-learnNext.js
Conservative Auto-Regeneration Policy for AI-Generated Financial Narratives
2025Completed

Conservative Auto-Regeneration Policy for AI-Generated Financial Narratives

Production validation system for AI-generated outputs in financial services — threshold optimisation, conservative AND escalation policy, priority-scored human review queue, and audit-grade provenance.

•Conservative AND policy: auto-regen only when both embedding similarity AND ROUGE-L fall below threshold — single-metric failure routes to human review
•Threshold calibration via bootstrap percentile sweep across labeled validation set
PythonNLPFinTech
Indian Desi Multilingual LLM — Training Pipeline
2025Completed

Indian Desi Multilingual LLM — Training Pipeline

End-to-end multilingual LLM training pipeline targeting Hindi/English code-switching. Dataset curation, LoRA fine-tuning, inference evaluation, and deployment packaging across 6 Kaggle notebooks.

•Canonical dataset curated from 3 complementary sources — chatbot dataset for tone, large-scale conversation corpus for diversity, sentence-pair dataset for structural grounding
•Unified schema normalising all sources into a clean, consistent format
PythonLLMNLP
Song Recommender System
2021Completed

Song Recommender System

ML-based workout song recommender using BPM and VADER sentiment analysis. Co-authored research with K-Means clustering on Billboard Top 100 to match songs to exercise intensity.

•Co-authored research project with Tanish Maheshwari (Presidency University)
•Lyrics extracted from Billboard Top 100 via Genius API and lyrics-extractor library
PythonMLNLP
Kaggle Portfolio
2020-PresentActive

Kaggle Portfolio

Notebooks Expert rank #2,441 / 59,663 — personal best #707. 34 notebooks, 11 datasets, 3 models, 1 competition entry. 10 bronze medals across ML, DL, NLP, Computer Vision, and regression.

•Notebooks Expert — current rank #2,441 / 59,663 | personal best #707
•34 published notebooks with 10 bronze medals
Data ScienceMLDeep Learning
Headlines Text Generation using LSTM
2021Completed

Headlines Text Generation using LSTM

Predicted and generated new headlines using NLP and LSTM networks.

•LSTM-based sequence model for natural language generation
•Trained on news headline corpus for next-word prediction
PythonNLPLSTM
Trigger Word Detection
2021Completed

Trigger Word Detection

Speech recognition system that detects the trigger word 'activate' using NLP and plays a chime sound upon detection.

•Detects trigger word 'activate' in audio stream using NLP
•Real-time speech recognition with chime activation on detection
PythonNLPSpeech Recognition
Face Recognition System
2020Completed

Face Recognition System

Built a face recognition system using OpenCV in Python — detects faces, eyes, smiles, and identifies individuals with confidence scores.

•Face detection with component recognition (eyes, smile)
•Person identification with confidence scoring against trained dataset
PythonOpenCVComputer Vision
Estadísticos — Stock Prediction & Classification
2020Completed

Estadísticos — Stock Prediction & Classification

Predicted Tesla stock prices using ML algorithms with descriptive, prescriptive, and predictive analysis. Also applied classification algorithms to PIMA India Diabetes dataset.

•Tesla stock price prediction using ML algorithms
•Descriptive, Prescriptive, and Predictive Analysis approaches
PythonMLClassification
Capabilities & Stack

Technical Focus & Technologies

AI & ML Systems

Focused capability layer

Evaluation pipelinesControl policiesDrift detectionFailure-mode metricsShadow deployment
Systems Readiness

The current focus blends high-leverage ML research with production-grade delivery, from evaluation surfaces to deployment governance.

Explicit Technology Stack

AI & Machine Learning
PythonPyTorchTransformersScikit-learnXGBoostKerasOpenCVTensorFlow
ML Systems & Reliability
LLM EvaluationAI ReliabilityModel GovernanceBehavioral TestingLLM GuardrailsPrompt EngineeringPSI & KL Drift
Enterprise Backend
JavaSpring BootREST APIsSQLOpenShiftJenkinsAnsibleLinuxJUnit

Credentials & Recognition

Kaggle Notebooks Expert

Rank 2,441 / 59,663 · Personal Best #707 · 10 Bronze Medals

B.Tech CSE — SRM IST

94.4% GPA · First Class with Distinction · 2018–2022

ML & Deep Learning Certified

Kaggle & Deep Learning Specialization · 2025

IELTS Certification

Band 8 / 9 · Dec 2025

Chronology

Engineering & Academic Timeline

MS in Artificial Intelligence

Fall 2026

Columbia University · New York, NY

Pursuing graduate studies in Artificial Intelligence to deepen my understanding of how modern AI systems learn, reason, and adapt — building on four years of enterprise software engineering and hands-on experience developing AI evaluation and reliability systems.

Software Engineer I A · SME

2024 - 2026

Bank of America

Promoted to Subject Matter Expert for CashPro Search, serving 550,000+ corporate users. Owned deployments, CI/CD, and release validation across 17+ cycles. Named inventor on a Bank of America patent (2026) for governed multi-modal AI architecture.

Software Engineer I B

2023 - 2024

Bank of America

Increasing ownership of backend services through feature development, production support, and service reliability. Diagnosed and resolved 20+ production incidents spanning application and infrastructure layers.

Apprentice

2022 - 2023

Bank of America

Built foundational experience in enterprise software engineering by contributing to the CashPro Search platform using Java, Spring Boot, and Agile delivery practices.

Kaggle Notebooks Expert

2022 - Present

Top 4.1% Globally

Notebooks Expert ranked #2,441 / 59,663 — personal best #707. 34 notebooks and 10 bronze medals across ML, DL, NLP, Computer Vision, and regression.

B.Tech CSE

2018 - 2022

SRM IST · 94.4% GPA

Graduated with 94.4% GPA in Computer Science. Built foundation in algorithms, data structures, and software engineering. Active in college tech/coding clubs and tech fests.

Interactive CLI

Systems Telemetry & Terminal

shubhankar@systems-cli:~
>welcome

SHUBHANKAR TIWARI — INTELLIGENT SYSTEMS TERMINAL v2.5.0

Type 'help' to see available commands or click quick suggestions below.

QUICK COMMANDS:
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Let's Build Reliable Systems

Open to opportunities in ML Systems, AI Infrastructure, Reliability Engineering, and Platform Architecture.

Send Email
GitHubLinkedInKaggleColumbia
st3907@columbia.edu•tiwarishubhankar@gmail.com