Engineering data-native AI systems.

• Secure login using OAuth 2.0 (AWS Cognito) so users authenticate safely without exposing raw credentials to the application.
• Token validation with JWKS-backed JWT verification, including signature + expiration checks (with JWKS fetching/caching to keep validation fast and reliable).
• Authorization enforcement using configurable role/group permissions (admin/member/viewer) mapped to specific REST API endpoints and HTTP methods (read/write/delete style actions).
• Implemented a reverse-proxy gatekeeper pattern so requests are routed only after the user is authenticated and cleared by the access rules.
• Designed role-permission configuration that can be updated via scripts and YAML/JSON rule sources, enabling “no downtime” style policy updates.
• Added automated testing for correctness and reliability: unit tests for login/role checks plus end-to-end speed tests for request responsiveness.
• Built CI/CD automation to run tests + build + deployment workflows, reducing deployment errors and improving operational stability.

Built a permissioned REST API access layer that keeps authentication and authorization consistent across services. The system verifies OAuth2 tokens, validates JWTs against Cognito JWKS, and enforces fine-grained, role-based access rules at the endpoint/method level through a configurable gatekeeper approach.

• Developed the AI memory backend using FastAPI with robust lifecycle management (async initialization, graceful cleanup hooks, shutdown resource handling).
• Created a modular API surface with thin routers delegating to deeper services, including real-time chat via an SSE streaming endpoint (`/api/v1/chat/stream`).
• Implemented embedding generation as a validated API (content-type checks + length validation) feeding into storage for vector similarity search.
• Built intelligence orchestration endpoints that trigger multi-entity processing via background jobs, keeping requests responsive instead of blocking threads.
• Added production-grade operational tooling for background jobs: enqueue/monitoring endpoints and incident-style flows (including emergency handling for stuck jobs).
• Integrated configuration-driven behavior for multi-environment support, optional Redis enablement, and production-safe rate-limiting defaults.
• Delivered a sizable automated test suite (API, concurrency, failure recovery, CORS behavior, and orchestration/integration coverage).
• Built the Next.js App Router frontend with a modern component stack, including a rich editor experience using TipTap + markdown support for structured content workflows.
• Implemented authenticated API proxying for streaming chat so the frontend can preserve `text/event-stream` response behavior and maintain real-time UX.
• Deeply integrated Convex at the schema layer for application state, background jobs, embeddings, and intelligence artifact persistence.
• Produced operational documentation for stateful systems (including migration workflows) so destructive/config changes are handled safely and repeatably.

Worked on a production-style “AI that remembers” platform: a backend that turns user interactions into persistent intelligence artifacts, plus a Next.js frontend with streaming-first UX. The system emphasizes modular APIs, real-time chat streaming, vector-backed state, and background jobs for long-running intelligence processing.

• Implemented an SDK/API layer (`run_optimization`) so backend services can trigger optimization without YAML workflows, including normalization and conversion into the internal optimization schema.
• Built an online runtime selection loop (select/update) designed for production behavior rather than offline experimentation.
• Used Thompson Sampling-style arm sampling to evolve decisions at runtime, updating cached arms with stability checks and reward feedback.
• Centralized Bayesian-style update plumbing so runtime performance signals translate into updated beliefs/estimates over time.
• Designed an extensible architecture with pluggable evaluators/adapters and multiple storage backends (sqlite/postgresql/multi-backend style).
• Supported multiple modes: batch evolutionary optimization (generations/population) and runtime per-request selection that improves while the system is live.
• Documented configuration formats and operational usage patterns so teams can adopt the framework consistently.

Contributed to an open-source Python framework that optimizes API/agent configuration over time. The project combines evolutionary-style search with reinforcement-learning-inspired control logic and supports both batch optimization runs and runtime per-request selection using multi-armed bandit strategies.

• Implemented the core parser to convert Vector-Native strings into structured operation objects using strict parsing (`parse_vector_native`) plus hybrid and fallback flows for real-world mixed LLM outputs.
• Added token counting and efficiency measurement with a tokenizer layer that uses `tiktoken` when available and falls back to heuristic estimation when not.
• Created token-reduction evaluation utilities (before/after + reduction %) so the protocol’s efficiency claims are measurable, not just descriptive.
• Added hybrid parsing support to preserve token savings while tolerating non-compliant or partially structured model outputs.
• Developed the project as a protocol-first system with formal documentation: symbol registry, syntax rules, validation rules, and extension guidelines.
• Set up test coverage for research claims, including parser/tokenizer behavior expectations and dedicated `tests/` coverage.
• Structured the project as implementation + measurement (parser/tokenizer + explicit reduction calculations) to enable repeatable experiments and reports.

Built a research/engineering system for a “Vector-Native” symbolic structured protocol that enables AI-to-AI communication with less ambiguity. The work includes a strict parser, hybrid parsing fallbacks, token-counting for efficiency measurement, and a full evaluation workflow so token savings/reduction can be proven and reproduced.