work artifacts
The artifacts are sanitized, but the structure is real: define the boundary, show the operating concern, describe the evidence, and leave behind something an engineer or security reviewer could use.
Use these lanes to scan the portfolio by the problem you care about: model risk, platform delivery, security proof, or architecture clarity.
I treat model-backed features like production software: inputs are bounded, tool calls are governed, failures are observable, and releases have rollback paths.
I move security work past scanner output by mapping the boundary, proving impact carefully, and translating findings into controls teams can actually ship.
I focus on the operating details that keep services reliable: deployment hygiene, Kubernetes ownership, rollback discipline, logs, metrics, and runbooks.
I turn ambiguous requirements into service boundaries, identity flows, data movement notes, rollout plans, and tradeoffs that engineers can build from.
portfolio
A production-oriented design for routing model traffic through identity-aware policy, retrieval checks, tool permissions, eval gates, logs, and replayable incidents.
Frames AI work as an operating system, not a prompt demo: every request has context, every tool action has a permission boundary, and every failure leaves evidence.
Boundary: Architecture is representative and sanitized. It does not publish private prompts, datasets, customer data, internal controls, or unsafe abuse detail.
Read artifactA delivery model for services that need predictable builds, Kubernetes readiness, progressive rollout, observability, ownership, and rollback instructions.
Makes release safety concrete: a service is not production-ready just because it deploys. It must be observable, recoverable, owned, and easy to reason about during failure.
Boundary: No private cluster names, customer services, credentials, environment details, or internal network diagrams are included.
Read artifactA report-building workflow that turns scoped testing into defensible evidence: request pairs, object-boundary checks, denied controls, impact notes, and remediation language.
Shows practical security judgment: boundary proof, impact clarity, false-positive reduction, and remediation guidance matter more than raw scanner output.
Boundary: Authorized testing only. No private target details, unsafe reproduction detail, secrets, customer data, or risky step-by-step instructions.
Read artifactAn operating model for model-backed products: trace fields, evaluation checkpoints, prompt and retrieval release notes, cost visibility, latency budgets, and incident review.
Bridges ML delivery, platform engineering, and security so model behavior can be measured, debugged, governed, and improved after it leaves a notebook.
Boundary: Examples are synthetic and sanitized. No private prompts, datasets, user conversations, internal traces, or customer content are published.
Read artifactArchitecture notes for enterprise platforms covering service boundaries, identity, data movement, integration risks, operational failure modes, and security controls.
Turns messy stakeholder language into a buildable plan: assumptions are visible, tradeoffs are named, ownership is clear, and risk is connected to implementation.
Boundary: Representative architecture language only. No internal diagrams, customer names, network details, or confidential implementation data.
Read artifact