Research Publication · 2017
Semantic Knowledge and Privacy in the Physical Web
Gurpreet et al.
Research work exploring semantic understanding and privacy considerations in physical web environments.
Read publicationLead AI Architect | AI Center of Excellence
Gurpreet Singh
I architect production AI systems from prototype to enterprise scale, combining technical depth with measurable business outcomes.
- 0+
- Years in production AI
- 0+
- Production models served
- 0.0%
- Platform uptime
- 0K+
- Agent workflows / month
Research
Selected papers & technical publications
Research foundations in privacy, dialog systems, and AI reasoning — with the full publication history on Google Scholar.
MS Thesis, UMBC · 2017
Context, Privacy and Spatial Grounding in Dialog Systems
Gurpreet
Graduate thesis focused on contextual grounding, privacy, and multimodal reasoning in conversational AI systems.
Read publicationScholar Index · 2026
Full Publications List
Google Scholar Profile
For the complete and current publication list, please refer to the Google Scholar profile.
Read publicationFlagship Projects
Systems led from concept to production
Architecture leadership, technical depth, and measurable business impact across multi-agent, code-generation, and MLOps platforms.
01Live
Multi-Agent Market Research Platform
Production multi-agent platform coordinating planning, analysis, and execution agents with real-time collaboration.
Live production system with specialized agent coordination for end-to-end market research workflows.
Multi-Agent SystemsLLM OrchestrationPythonReact
02Live
Drawing-to-Code Generation System
AI-assisted application generator that converts hand-drawn wireframes into functional full-stack applications.
Demonstrates design-to-implementation acceleration through AI-driven code synthesis.
Code GenerationLLMsFull-StackProductivity
03Reference
Enterprise MLOps Platform
Comprehensive ML lifecycle platform for model versioning, CI/CD, drift monitoring, testing, and operational rollback.
Supported 100+ production models with 99.9% uptime across real-time distributed services.
MLOpsCI/CDMonitoringDistributed Systems
Video
Talks & technical deep dives
Architecture, deployment patterns, and practical strategy for teams shipping AI in production.
Writing
Essays on AI strategy & execution
Decision logs and thought leadership for teams that need to make AI calls with technical and commercial clarity.
Technical Notes · 2025
Architecture Decision Log: Multi-Agent Coordination Patterns
Comparison of orchestration strategies, fault boundaries, and trade-offs for production agent systems.
Read articleTechnical Notes · 2026
Architecture Decision Log: RAG Stack Trade-offs
Evaluation of retrieval architectures, index strategies, and latency-cost-quality balance in enterprise workloads.
Read articleExperience
A leadership track in AI & ML platforms
Building and leading teams that ship production AI in enterprise environments.
Lead AI Architect, AI Center of Excellence
Jan 2026 - Present
Current Organization
- Lead enterprise AI architecture strategy and execution across platform, research, and product functions.
- Define standards for model governance, evaluation, and deployment reliability across mission-critical use cases.
- Drive AI Center of Excellence initiatives to accelerate adoption, improve quality, and de-risk delivery.
Senior ML and LLM Engineer (Director of R&D)
Dec 2021 - Jan 2026
Nantum AI
- Architected MLOps infrastructure for 100+ production models with CI/CD, drift monitoring, rollback, and 99.9% uptime.
- Designed and deployed a multi-agent orchestration system processing 5K+ workflows per month with graph and RAG pipelines.
- Built monitoring and observability stack reducing mean time to detection from hours to minutes.
- Developed intelligent sensor labeling pipelines across 200+ deployments, reducing onboarding from days to hours and manual cost by 60%.
Data Scientist and ML Engineer
Dec 2017 - Dec 2021
Nantum AI
- Architected distributed anomaly detection platform monitoring 1,000+ systems across diverse built environments.
- Developed hybrid optimization models delivering 20% energy cost reduction for commercial clients.
- Implemented A/B and shadow-deployment framework with real-time ROI reporting for stakeholders.
Deep Learning Engineer
Aug 2017 - Dec 2017
QueueHop Inc.
- Led computer vision system for autonomous retail checkout using CNN-based object detection and recognition.
- Delivered sub-100ms inference latency in production-oriented deployment settings.
Capabilities
Core technical & leadership strengths
A blend of deep systems engineering and strategic execution for modern AI organizations.
01
LLMs and Generative AI
RAGFine-Tuning (LoRA and QLoRA)Multi-Agent SystemsTransformersOpenAI and Claude APIsVector DBs (FAISS and Pinecone)
02
ML and Data Engineering
PyTorchHugging FaceTime-Series ModelingDistributed Inference
03
MLOps and Infrastructure
MLflowAWS (ECS, Lambda, S3)SageMakerDockerCI/CDA/B TestingModel Monitoring
04
Leadership
AI Architecture StrategyCenter of Excellence LeadershipCross-Functional DeliveryTechnical MentoringRoadmapping and Governance
Contact