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Srikumar Nayak

NYC, United States

I build AI platforms and products that ship and scale —setting strategy, architecting the stack, leading teams, and getting hands on when it moves the mission.

AI PhD, Harvard AMP–trained, and a former Visiting Scholar at Stanford AI Lab, I serve as Principal AI Architect . With 15+ years across financial services, Financial crime enforcement solutions and large?scale systems, and cloud (AWS/Azure/GCP), I bridge research and applied engineering to deliver measurable business impact—safely and at scale.

How I lead
• Set north star AI strategy, roadmaps, and operating models; align OKRs with product and P&L.
• Build and scale orgs; hire, mentor, and uplevel senior ICs and managers.
• Drive cross functional execution with Product, Security, Legal/Privacy, and GTM.
• Embed Responsible AI (governance, risk, fairness, privacy) into the SDLC.

What I build
. Robust NextGen AI/LLM Fraud and Anti money Laundering enterprise algorithm ,models and solutions.
• GenAI/LLM platforms: RAG, fine tuning, evaluation & guardrails, agents, vector stores.
• MLOps/LLMOps: model CI/CD, feature stores, registries, canary & A/B testing, observability.
• Cloud?native (AWS/Azure/GCP): microservices, streaming, APIs; cost/perf optimization and SLOs.
• Security & compliance: PCI compliant fintech solutions; secure data & model pipelines.

Highlights
• Collaborations with Stanford AI Lab and MIT CSAIL; advisor to the C?suite across Fortune 100 and startups.
• Community leadership with ACAMS , IEEE , ACM, AI
• Adjunct faculty & frequent conference speaker; STEM robotics coach.

Keywords
AI Strategy • GenAI • LLMs • RAG • Agents • Evaluation • Guardrails • Responsible AI • Governance/Risk • MLOps/LLMOps • Model Registry • Feature Store • Observability • A/B Testing • AWS • Azure • GCP • Microservices • Streaming • APIs • Data Engineering • Vector DBs • Prompt Engineering • FinTech • PCI DSS • Security & Privacy • Cost Optimization • SLOs/OKRs • FinOps • Context Engineering

Available For: Advising, Authoring, Consulting, Influencing, Speaking
Travels From: NYC

Srikumar Nayak Points
Academic 5
Author 1
Influencer 0
Speaker 0
Entrepreneur 0
Total 6

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Company Type:
Minimum Project Size: Undisclosed
Average Hourly Rate: Undisclosed
Number of Employees: Undisclosed
Company Founded Date: Undisclosed

Areas of Expertise

Agentic AI
AI 30.08
AI Ethics
AI Governance 32.14
AI Infrastructure
AI Orchestration 37
Architecture
Big Data
Blockchain
Cryptocurrency
Cybersecurity
Data Center
Design Thinking
Digital Transformation
Emerging Technology
Engineering
FinTech
Generative AI

Industry Experience

Publications

1 Article/Blog
COMPLIANCE-AWARE MACHINE LEARNING PIPELINES: ANALYTICAL MODELLING OF REGULATORY CONSTRAINTS IN AUTOMATED DECISION SYSTEMS
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
October 01, 2025
The increasing reliance on artificial intelligence (AI) for high-stakes decisions has heightened the importance of the regulatory compliance of its usage, ethical responsibility, and transparency in the algorithms. The presented paper proposes a structure that enables compliance-conscious machine learning pipelines, which is intended to introduce legal, ethical, and governance restrictions in the ML lifecycle. In contrast to the post-hoc auditing methods, the suggested model incorporates compliance on the algorithmic level by restraining regularization by constraints and rule validation based on logic. The framework was empirically tested with the aid of the AI Fairness Dataset (COMPAS subset) suggested in Kaggle to measure the trade-offs between the model performance, fairness, and traceability. The findings indicate that compliance research in the form of compliance-based prediction accuracy decreases by less than 3%, and fairness compliance increases by more than 20% with almost complete audit traceability. Additionally, a Continuous Compliance Learning (CL) mechanism is a part of the MLOps loop that guarantees adaptive compliance with the changing regulations like GDPR, ISO/IEC 23894:2023, and the EU AI Act. The research proves that compliance is a mathematically optimizable co-objective to balance predictive efficiency and legal and ethical requirements. This contribution contributes to the explainable and law-aligned AI paradigm, which allows responsible application to the controlled areas of finance, healthcare and recruitment.

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Tags: AI, AI Governance, AI Orchestration

1 Journal Publication
Converging AI innovation and quantum security for data-driven compliance, financial crime re-regulation
World Journal of Advanced Research and Reviews
November 10, 2025

This study discusses the use of classical and quantum machine learning models to detect fraudulent bank transactions. Random Forest model was tested on credit card fraud detection data set and scored large percentage 99.95, AUC-ROC score/ROC is 1.0 and F1 scores are high. The most influential predictors were identified to be key features including the amount of transaction, periods between transactions, and location. In order to avoid the problem of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized, which enhanced the work of the model. Another promising study of quantum hardware scalability limits, but with multiple serious limitations, was the Quantum Support Vector Classifier (QSVC), which faces difficulty in qubit coherence and scalability challenges. These limitations did not allow the model to effectively process large data sets to better accommodate real world applications. Nevertheless, quantum models have the potential to improve the fraud detection system with developing quantum technology. This study brings out the usefulness of Random Forest in detecting fraud cases and outlines the opportunities of quantum models in the future, recommending future research, such as quantum-classical hybrid models, and the enhancement of quantum computers to meet real-time needs.

See publication

Tags: AI, AI Governance, AI Orchestration

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