Solution Architect – LLM Backend & Vehicle Interface

Own and evolve the system architecture of the backend service: routing, business logic, orchestration, service decomposition, and the structural discipline (hexagonal architecture / ports and adapters) that keeps the platform changeable over a multi-year lifecycle.

Define and govern the interface concept between vehicle, LLM, tool services and agents — the formal contract layer, its versioning strategy, and its evolution across vehicle generations.

Architect the vehicle-facing integration: REST and gRPC/protobuf APIs, Viwi, AIDL toward the Android Automotive side, OAuth2 and mTLS, client IdP integration, and the client's internal host and telemetry interfaces.

Design the streaming architecture — migrating AI service calls to streaming for responsiveness: incremental chat completion, real-time ASR transcription, TTS playback during synthesis; with buffering, connection management, error handling, and clean cancellation/interruption semantics (the foundation for barge-in).

Own the LLM integration layer: prompt normalisation and pre-/post-processing, response format control for vehicle display, context preparation and dialogue management, and deterministic vehicle answers via system prompts and a guardrails engine — in a domain where a wrong answer is a safety and brand issue, not just a quality one.

Design the agent orchestration mechanism that decides whether a request is answered by the LLM, an internal tool, or an external agent — including multi-intent handling and the routing model behind it (LangGraph).

Architect tool and agent integrations: navigation with semantic location resolution, media/entertainment search, knowledge queries, calendar and mail, POI and places services, and vehicle data services.

Define the RAG and persistence architecture: embedding strategy and lifecycle, vector store design (PostgreSQL/pgvector), schema design, tuning and migrations, and the division of responsibility between relational, document and vector stores.

Produce scaling and load concepts for series operation, and the security, data-protection and compliance concept per automotive standards — including data minimisation in telemetry and traces.

Own the observability architecture end to end: OpenTelemetry distributed tracing, LangFuse for prompt tracing and evaluation, Azure Monitor metrics and dashboards, alert thresholds and routing — so latency can be attributed per component and failure chains reconstructed.

Make the architecture hold its operational targets: high availability in the 99.5% SLO range, incident resolution inside 24 hours, business-hours 2nd/3rd-level support, and structured version, release and deployment management.

Guarantee backward compatibility for existing vehicle generations — vehicles from model year 2021 onward must keep working as new features ship.

Set and enforce engineering standards with the development teams: API and interface review, Python toolchain and code quality gates, CI/CD pipeline design on Azure DevOps (including self-hosted runners) and GitHub, IaC module structure in Terraform/Terragrunt.

Act as technical counterpart to the client's architects and to the vehicle, backend and UX teams; maintain architecture documentation in Confluence and work within the client's requirements management tooling; provide technical direction to the distributed development and AI Ops team in Scrum.