// What I do
Three ways to bring reliability to your platform and AI.
Each engagement applies the same discipline: understand the system, design for failure, measure what matters, and leave it more reliable than I found it.
Cloud Platform & Site Reliability Engineering
The problem
Platforms grow faster than the practices that keep them stable. Without SLOs, observability, and repeatable infrastructure, reliability becomes guesswork and every incident is a fire drill.
What I bring
The SRE discipline that kept advisor-facing platforms running at Wells Fargo Advisors — applied to your environment, your constraints, your team.
Led site reliability engineering for high-availability, regulated hybrid-cloud platforms serving 15,000+ advisors. SLO design, incident management, and observability in an environment where reliability and compliance are non-negotiable.
Capabilities
- SLI/SLO design and error-budget policy
- FMEA and failure-mode analysis
- Incident detection, triage, and post-incident review
- Observability (Azure Monitor, Splunk, AppDynamics)
- Infrastructure as Code (Terraform, ARM/Bicep) with environment parity
- Kubernetes / AKS operations
- CI/CD reliability gates and rollback automation
- Disaster recovery and resiliency design
AI Systems in Production
The problem
A model that works in a notebook is not a system that works in production. Latency, cost, retrieval quality, and failure handling decide whether an AI initiative survives real users and audits.
What I bring
The same SRE reliability discipline — SLOs, error budgets, incident runbooks — applied to AI-native systems, backed by an M.S. in AI/ML with Distinguished Honors.
Built and operated production AI systems — including a multi-agent orchestration pipeline and a RAG system — applying the same reliability discipline used for traditional infrastructure. M.S. in AI/ML, Distinguished Honors (3.97 GPA), June 2025.
Capabilities
- Production RAG pipelines with vector search (FAISS)
- Multi-agent orchestration systems (LangGraph)
- Databricks ML pipeline development
- LLM production deployment with latency and cost monitoring
- Reliability engineering specific to AI-powered applications
Fractional & Contract Engineering
The problem
Not every team needs a full-time hire. Sometimes you need senior reliability judgment for a defined period — or clarity on whether you're ready for a larger AI initiative before committing budget.
What I bring
A diagnostic-first, low-commitment entry point: start with an AI Readiness Audit and expand only if it makes sense.
Most engagements start with an AI Readiness Audit — a fast, low-risk way to see where you stand before larger commitments. No pitch deck, no proposal until I understand the problem.
Capabilities
- AI Readiness Audit — platform and data readiness diagnostic
- Fractional / contract Staff-level reliability and platform work
- Regulated-environment delivery (compliance-aware architecture, audit logging)
- C2C / staffing-partner engagements
Ready to talk about your situation?
Tell me what you're building and what worries you about running it. We'll figure out the right way to work together.