Mikiyas Gebrehaweriya

Co-founded the company and lead the technical side of it: architecture, engineering and delivery across an AI-native product development platform of six deployables and 274k lines.

• Architected and built a NestJS modular monolith: 37 modules, 57 controllers, 337 REST endpoints, backed by a 69-model Prisma schema across 62 migrations.
• Designed the multi-tenancy model — organization_id on every business row, AsyncLocalStorage request context, per-module scope guards, and an append-only audit log enforced by Postgres rules rather than convention.
• Built a Python/FastAPI AI orchestration service with layered best-effort context assembly, model tier routing, output budgets and Redis idempotency, so retrieval failures degrade answers instead of breaking streams.
• Made RBAC bypass structurally impossible for the AI: tools call back into the Core API carrying the caller's own JWT, so every existing guard applies unchanged.
• Implemented retrieval on Postgres and pgvector — a vector(1024) HNSW cosine index over an entity/edge knowledge graph — behind an Embedder interface with a deterministic offline mock for tests.

TypeScript · NestJS · Python · FastAPI · PostgreSQL · pgvector · Prisma · Redis · BullMQ · Next.js · React · Docker · Turborepo · Stripe · Socket.IO · OpenTelemetry · Anthropic · MCP

Built a unified operational data store for social activity with hybrid search and a store-aware natural language assistant.

• Designed a polyglot persistence layer — PostgreSQL for relations, MongoDB for raw documents, a vector store for semantics — rather than forcing one engine to cover all three access patterns.
• Implemented Bronze, Silver and Gold ingestion stages so raw payloads stay immutable and every enrichment is reproducible by replay rather than by patching production data.
• Built hybrid retrieval fusing lexical and vector results, which handles exact-name lookups and vague conceptual questions in the same query path.
• Ran async enrichment through Celery and Redis, and containerised the full stack so a clean machine is one compose command from working.

Python · Django · DRF · PostgreSQL · MongoDB · Celery · Redis · LangChain · Docker

Won the Customer Relations track with an AI-powered customer relations hub built around lossless human handover.

• Took first place in the Customer Relations category against competing university teams.
• Designed the agent workflow end to end: multi-modal intake, semantic retrieval over Porsche's own documentation and service records, tool-based lookups, satisfaction check, and escalation.
• Argued for and designed the escalation path first — the human agent inherits the full transcript, retrieved sources and the system's own confidence, so nothing the customer already said is repeated.
• Designed the CR staff console: live session tracking, resolution KPIs, and a queue ordered by urgency rather than arrival.
• Presented the final solution to Porsche Digital stakeholders.

Python · AWS · Vector database · Embeddings · RAG · Multi-modal UX