Quantum AI Researcher

Amol
Deshmukh

PhD in Physics. 7+ years turning complex data into production ML systems, Quantum and AI applications, and quantitative models in financial services.

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New York, NY IBM Corporation PhD Physics, CUNY
Amol Deshmukh
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Career

Professional Experience

From quantum physics research to production ML systems at scale.

IBM Corporation
Oct 2020 — Present
Computational Scientist
  • Engineered a RAG-based Q&A agent using LangChain and IBM Granite to synthesize complex quantum applications research from Qiskit documentation.
  • Delivered a hybrid quantum–classical credit-default prediction model prototype, achieving 0.4% improvement against XGBoost baseline across AUC and default-rate.
  • Benchmarked quantum ML algorithms (RNNs/LSTMs, probabilistic models, clustering) against SOTA classical ML to identify scalability limits and computational bottlenecks.
  • Provided quantum computing expertise to identify clients' key computational challenges and co-design GenAI and quantum computing use cases with near-term, high-ROI potential.
  • Built a comprehensive benchmarking suite comparing classical and quantum ML and GenAI techniques for financial modeling workflows.
LangChain RAG GenAI Quantum ML XGBoost PyTorch
EXL / Barclays Bank
Dec 2018 — Oct 2020
Data Scientist
  • Refined customer segmentation for 11M credit-card users using k-means, improving inter-cluster delinquency separation by 300% and profit separation by 150%.
  • Built a revolver propensity model for a co-branded Uber credit card using stacked ensemble methods, optimizing approval rate and profitability.
  • Performed feature engineering on large-scale bureau and transactional datasets, improving model interpretability and regulatory compliance for risk models.
  • Led a team of 2 data scientists to create transaction data ETL pipelines from internal Oracle Server to Amazon S3, analyzing it with PySpark on AWS (~80% reduction in processing time).
k-Means Ensemble Methods PySpark AWS SQL ETL
The Graduate Center, CUNY
Sep 2014 — Sep 2018
Graduate Research Assistant
  • Modeled quantum properties of baryon systems using effective field theory, applying advanced computational techniques to solve high-dimensional statistical physics problems.
  • Developed Monte Carlo simulation tools (Metropolis algorithm) in Python and C.
Monte Carlo Python C Field Theory Statistical Physics
Toolbox

Technical Skills

A blend of software engineering, machine learning, and deep quantitative foundations.

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Programming & Tools

PythonNumPyPandasScikit-learnSQLGit / GitHubPyTorchPySparkUnix
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ML & AI

Deep LearningClusteringGenAIRAGsAgentic AILangChainVector Databases
4

Statistics

Bayesian ModelingHypothesis TestingANOVABootstrapping
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Quantitative Methods

Linear AlgebraProbabilityNumerical OptimizationStatistical Modeling
5

Cloud & Containers

IBM CloudAWSGCPKubernetesRedHat OpenShift
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Quantum Computing

QiskitQuantum MLHybrid AlgorithmsBenchmarking
Education

Academic Background

A strong quantitative foundation from theoretical physics to applied data science.

Ph.D., Physics
The Graduate Center, CUNY — New York, NY
September 2018
B.S./M.S., Physics
IISER Mohali — India
May 2012
Get in Touch

Let's build something remarkable

Interested in ML, GenAI, or quantitative modeling? I'd love to hear from you.

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