Prem Moola

Building a private enterprise AI platform focused on secure AI adoption, agent orchestration, developer productivity, and governed tool execution.

• Built a private enterprise AI platform for regulated environments, combining secure inference, RAG, governed tooling, observability, and policy-aware execution to support production AI adoption
• Designed infrastructure for multi-agent collaboration, tool orchestration, and AI-native developer workflows aimed at improving enterprise engineering productivity
• Established an architecture for security-sensitive organizations that need controlled AI adoption without exposing data, prompts, or execution context to third-party services
• Shaped the platform around private deployment models, auditability, and governed access patterns required by enterprises adopting AI in regulated environments
• Integrated retrieval, execution, and feedback loops into a cohesive platform model that supports both developer productivity and enterprise control

Building an AI-native SaaS product company across productivity, property operations, and workforce management.

• Built and launched multiple SaaS products including Diggt Habit, Surelease, and Shyftly across distinct operating domains and operating models
• Established shared cloud-native platform components, deployment patterns, and reusable services to accelerate delivery and reduce duplication across products
• Integrated agent-assisted workflows, automation patterns, and AI-native product experiences to improve usability, operating efficiency, and product differentiation
• Led product architecture, infrastructure, engineering execution, and technical direction across multiple products as founder and technical operator
• Created a common technical foundation spanning application architecture, cloud operations, release patterns, and shared services across the product portfolio
• Balanced founder-led product iteration with platform discipline, enabling faster product launches without fragmenting the underlying technical stack

• Defined enterprise Alternatives Data Strategy, aligning platform modernization with compliance, operating efficiency, and business priorities
• Led modernization of the alternatives data platform, improving scalability, consistency, lineage, and downstream reporting quality
• Standardized ingestion from multiple accounting engines into a unified schema and data model, reducing reconciliation time by 40%
• Delivered conversational analytics capabilities on top of governed enterprise data, reducing time-to-insight by 70% for business users
• Introduced MCP-based agentic AI workflows for data validation and analytics use cases, extending AI from experimentation into enterprise operations
• Strengthened the platform foundation for alternatives reporting by improving data quality controls, schema consistency, and operational reliability across upstream sources
• Connected data strategy, platform modernization, and AI-enabled access patterns into a more usable operating model for business and analytics teams

• Directed engineering for a cloud SaaS platform supporting professional media workflows, high-volume media pipelines, and enterprise customers
• Reduced infrastructure costs by 80% through architecture optimization, cloud efficiency improvements, and vendor renegotiation, materially improving operating leverage
• Maintained 99.99% uptime across pipelines and infrastructure supporting global customers and demanding production workloads
• Integrated with Adobe, Final Cut Pro, and DaVinci Resolve, reducing editorial turnaround time by 50%
• Helped drive $8M ARR through camera-to-cloud partnerships and workflow integrations with strategic ecosystem partners
• Led engineering across platform reliability, workflow integrations, delivery execution, and customer-facing product capabilities in a fast-moving SaaS environment
• Improved the technical and operational foundation needed to support enterprise customers with demanding performance, reliability, and workflow requirements

• Built private cloud infrastructure using VMware and Docker, reducing hosting spend and improving scalability
• Designed distributed analytics platforms using Hadoop, Spark, HDFS, and Elasticsearch for enterprise data processing and analytics workloads
• Increased deployment velocity 5x through SDLC standardization, CI/CD automation, and engineering workflow improvements
• Led a 40-member engineering organization across onshore and offshore teams
• Established a more scalable engineering operating model by aligning platform engineering, delivery processes, and infrastructure automation

• Managed a 46-node Sybase IQ cluster and reduced operational costs by 75%
• Built enterprise data platforms using Hadoop, Spark, and MemSQL for analytics, large-scale reporting, and regulatory workflows
• Delivered high-volume, low-latency systems supporting global trading and risk operations
• Directed regulatory technology supporting CFTC, ESMA, JFSA, NSD, and Canada reporting requirements
• Combined large-scale data engineering, regulatory execution, and operational reliability to support critical front-office and reporting workloads
• Built a strong foundation in enterprise architecture, distributed systems, and regulated data platforms within a global financial institution