Private Equity Technology Leadership
I do not just lead teams. I lead outcomes. In Private Equity environments where time is capital and clarity is currency, I bring the execution mindset required to scale. I partner directly with PE advisors, operating partners, and executive leadership to align engineering velocity with business value.
Strategic growth to 175+. Through a PE growth phase I scaled one SaaS engineering organization from a smaller base to 175+ engineers across onshore, nearshore, and offshore, with 80% engineering growth in 9 months without breaking culture - hiring plan tied to product roadmap, not headcount theater. PE is where this scale story actually belongs: the business demanded it, the thesis required it, and the engineering org delivered 99.95% SLA at sub-second response for 10k concurrent users, code-to-release cycle time down 40%, and a greenfield agentic supply chain intelligence platform on Python FastAPI and vLLM in production. My broader sweet spot is engineering teams of 10 to 60 - growth-stage inflection points where every hire compounds - but I have built and run the bigger org when the deal thesis required it.
I present architecture and investment cases directly to PE advisors and the C-suite - and back them with measurable outcomes. I have led post-merger integration and portfolio rationalization, secured offshore investment for new platform builds, and operated under board scrutiny across multiple PE-backed companies.
Engineering translated into business language: deploy frequency, MTTR, SLA attainment, and AI ROI - not sprint burndown charts.
Talk PE Strategy
What PE Firms Need from a Technology Leader
Execution Under Scrutiny
I operate under board-level scrutiny and investor pressure. I translate engineering complexity into clear business impact - the language PE advisors and operating partners need.
Measurable ROI
Every technology initiative I lead is tied to measurable business outcomes: deployment velocity, incident reduction, cost savings, uptime SLAs. No vanity metrics.
M&A Due Diligence
I conduct thorough technical due diligence on acquisition targets - assessing platform scalability, technical debt, team quality, and integration complexity before the deal closes.
Rapid Value Creation
PE timelines demand rapid value creation. I hit the ground running with proven frameworks for platform assessment, team evaluation, and 100-day transformation plans.
Transformation Outcomes
Real results delivered for PE portfolio companies under real-world constraints.
Global Engineers Scaled
Onshore, nearshore, and offshore engineering organization grown 80% in 9 months through a deliberate global talent strategy and a culture of autonomy with accountability.
SLA at Sub-Second
Re-architected service orchestration and application routing for 10k concurrent users at sub-second response. MTTR cut 30% via Datadog and Splunk observability standards.
Open-Weight Model in Production
Greenfield agentic supply chain intelligence platform on Python FastAPI and vLLM, combining contract performance with utilization analytics.
AI ROI Accountability
CFOs now demand tangible ROI from AI investments. I build the accountability frameworks that prove it - tracking cost-per-agent, automation ROI, and efficiency gains with the same rigor as infrastructure spend.
No AI theater. Measurable outcomes only.
- FinOps for Agents
I track AI agent costs the same way infrastructure costs are tracked: cost-per-workflow, cost-per-automation, ROI per agent deployment.
- Agent Reliability SLOs
Agents have SLOs just like microservices - uptime, accuracy, latency, and human escalation rates. Board-ready dashboards, not vague "AI adoption" slides.
- Engineering Velocity Metrics
MTTR, SLA attainment, deploy frequency, PR throughput, onboarding time-to-productivity, and AI-assisted deployment rates - quantified, trended, and tied to business outcomes.
100-Day Transformation Framework
Diagnose (Days 1-30)
Comprehensive platform audit, team assessment, and technical debt quantification. I map the current state with precision - architecture risks, key-person dependencies, security gaps, and scalability constraints - without disrupting ongoing delivery.
Stabilize (Days 31-60)
Address the highest-risk findings from the diagnostic phase, establish platform health baselines, implement quick wins that demonstrate ROI, and build the operating cadence that will sustain the transformation through the acceleration phase.
Accelerate (Days 61-100)
Execute the first phase of the modernization roadmap, deploy a multi-model AI strategy across Claude Code, GitHub Copilot, AWS Kiro, and OpenAI Codex as first-class CI/CD pipeline stages, establish board-ready reporting on engineering velocity and AI ROI, and position the platform for sustained value creation.
Technical Due Diligence Checklist
My due diligence assessments cover six critical dimensions that PE firms need to understand before closing a technology acquisition.
Platform Scalability
Architecture review for scalability constraints, load testing results, database bottlenecks, and the investment required to support 5-10x user growth.
Technical Debt Depth
Quantified technical debt assessment: how much investment is required to modernize, and what velocity tax is the debt imposing on the engineering team today.
Team Quality & Retention Risk
Engineering team capability assessment, key-person dependency mapping, attrition risk analysis, and culture evaluation for post-acquisition integration.
Security Posture
Security architecture review, vulnerability scanning results, compliance status (SOC 2, PCI, HIPAA), and identification of post-acquisition liability exposure.
Integration Complexity
Assessment of integration effort required to connect the acquisition with existing portfolio technology - APIs, data models, identity systems, and operational tooling.
AI Readiness
Assessment of whether the platform architecture can support agentic AI integrations - the key driver of engineering velocity gains in 2026 and beyond.