Soniya Singh, MBA
I didn't choose the intersection of technical depth and business strategy, it chose me. With a B.Sc. in Computer Science as my foundation, I spent eight years watching companies build extraordinary AI systems that nobody could buy, explain, or trust.
That gap became my obsession. as an AI/ML Product Strategy Architect at AI Blockchain INC., I lead LLM-driven product development, agentic AI systems design, ML inference optimization, and cost-efficient cloud-native deployments, owning the full arc from strategy to production.
My Emory MBA at Goizueta deepens what I already practice: connecting AI innovation to enterprise strategy, responsible governance, and scalable go-to-market execution. Because the world doesn't need more demos, it needs products that work, scale, and win markets.
Product Strategy & Roadmap
Translating emerging AI capabilities into enterprise-ready product roadmaps through customer discovery, competitive positioning, and cross-functional alignment across engineering, GTM, and executive stakeholders.
Discovery | Prioritization | Competitive Positioning
Cloud-Native AI Infrastructure
Hands-on AWS expertise deploying scalable AI workloads using containerized microservices, serverless architectures, CI/CD pipelines, and GPU-optimized environments, bridging infrastructure with product performance.
AWS | Containers | Serverless
LLMs & Agentic AI Delivery
End-to-end ownership of LLM-powered products and autonomous agent systems — multi-agent orchestration, tool use, memory architectures, RAG, and iterative agent performance optimization at enterprise scale.
RAG | Multi-agent| Prompt Engineering
AI Governance & Responsible AI
Designing responsible AI frameworks covering model transparency, bias detection, access control, data privacy, audit logging, and compliance guardrails, without sacrificing speed to
market.
Bias Detection | Compliance |
Audit
AI/ML Systems & Model Lifecycle
Deep experience across the full ML lifecycle — from data preparation and model selection to evaluation, deployment, and performance monitoring for classical ML, deep learning, and production AI pipelines.
MLOps | Inference |
Evaluation
Technical Product Marketing
Closing the gap between what AI systems do and what buyers understand, category creation, positioning strategy, developer GTM, and launch execution for LLM, agentic, and infrastructure
products.
GTM | Positioning | Category Creation
