Naman Pratyush
Engineering Executive Building AI-Enabled Products, Platforms and High-Performing Teams
Technology and AI strategy leader with a track record of aligning engineering organizations with enterprise business objectives. Partners with executive stakeholders to define technology roadmaps, modernize legacy platforms, and lead cross-functional teams through complex transformations. Currently driving responsible AI adoption—from strategy through production—building on deep foundations in cloud architecture, distributed systems, and engineering operations. Takes initiatives from concept to measurable business outcomes with disciplined execution and organizational leadership.
Executive Mandate
I lead engineering organizations through platform modernization, AI adoption and operational transformation—connecting business priorities with architecture, execution and measurable outcomes.
Focus areas: Enterprise AI strategy and enablement, cloud and platform modernization, engineering organizational scale, technology governance, and responsible AI implementation across regulated and commercial environments.
Leadership Scope
AI Strategy and Enablement
Defining enterprise AI roadmaps, evaluating build-vs-buy decisions, and establishing governance frameworks for responsible AI adoption across business units.
Engineering Organization Leadership
Building and scaling high-performing engineering teams, establishing delivery models, and mentoring technical leaders to drive accountability and growth.
Technology Roadmaps and Governance
Setting multi-year technology direction, prioritizing investments, and governing architecture decisions to align with business strategy and risk appetite.
Cloud and Platform Modernization
Leading migration of legacy monolithic systems to modular, cloud-native platforms—improving scalability, cost efficiency, and developer velocity.
Product and Business Partnership
Collaborating with product, editorial, and commercial leaders to translate business requirements into engineering priorities and measurable outcomes.
Operational Excellence and Risk Management
Establishing reliability standards, observability practices, and security controls to ensure sustained platform performance and regulatory compliance.
AI Strategy
Positioned to lead an organization's AI initiative end-to-end—not only experimenting with tools, but building the strategy, architecture, governance, and delivery practices required to move AI from proof of concept to production.
Enterprise AI Strategy and Roadmap Development
Defining AI investment priorities aligned to business outcomes, sequencing initiatives, and building organizational capability roadmaps for sustainable adoption.
Generative AI and LLM Application Architecture
Designing production-grade LLM application architectures—prompt engineering layers, context management, output validation, and integration patterns for enterprise systems.
Retrieval-Augmented Generation and Vector Databases
Building RAG pipelines with vector stores, embedding strategies, and retrieval optimization to ground model outputs in verified enterprise knowledge.
AI Agents, Tool Use and MCP-Based Integrations
Developing agentic workflows with tool-use capabilities and Model Context Protocol integrations to connect AI systems with enterprise data sources and services.
AI Governance, Acceptable-Use Policies and Data Protection
Establishing governance frameworks, acceptable-use policies, and data protection controls to ensure responsible and compliant AI deployment.
Model and Vendor Evaluation
Evaluating models and vendors across capability, cost, latency, and compliance dimensions to inform build-vs-buy decisions and reduce vendor lock-in risk.
Human-in-the-Loop Workflows
Designing review and approval workflows that maintain human oversight for high-stakes decisions, balancing automation with accountability.
Secure Deployment, Observability and Cost Management
Deploying AI systems with monitoring for quality drift, cost tracking, and security controls to maintain performance and budget discipline in production.
From Proof of Concept to Production
Building the delivery pipeline, evaluation criteria, and operational practices needed to transition AI initiatives from experimentation to reliable production services.
Career Impact
Professional Experience
Entrepreneurial Leadership
Building a trusted digital platform that connects domestic-violence survivors with lawyers, shelters, and verified support resources across Canada and the United States. A purpose-driven side initiative, separate from full-time employment, focused on responsible technology for vulnerable users.
- Product strategy and platform direction—defining the end-to-end user journey from discovery to connection with verified providers.
- Responsible technology for sensitive users—designing with trauma-informed principles, safety, and accessibility at the core.
- Privacy, trust and safety—building privacy-first architecture to protect user identities and sensitive personal data.
- Provider and lawyer onboarding—creating verification workflows and onboarding pipelines for legal professionals and support organizations.
- Search, directory and content architecture—structuring resource directories, geographic search, and content taxonomy for discoverability.
- Partnership and geographic expansion strategy—planning staged rollout across Canadian and U.S. jurisdictions with regional partner integration.