Solution Architect – LLM Backend & Vehicle Interface; w​/m​/d; strong Python, hands

Our client is advancing its in-vehicle voice assistant into an intelligent, AI-powered companion. Large-language-model capabilities (Azure OpenAI / ChatGPT) have been running in production across vehicles with new E³-architecture models featuring enhanced voice functions from the factory. The customer backend is the cloud AI orchestration service behind this: it receives requests from the vehicle, classifies and routes them, orchestrates the LLM, tool services and specialised agents, and returns an answer or action to the car.

DXC Luxoft serves as the end-to-end delivery partner, working in a joint product team with the client's engineers on the Azure platform (AKS, Azure OpenAI, AI Foundry, Managed Identity, Azure Monitor, Azure Dev Ops).

This role owns the architecture of the backend service in series operation — a system that is already live across a large vehicle fleet and must stay available and backward-compatible while significant new capability is added: streaming across the full ASR → LLM → TTS chain, multi-intent handling, barge-in, a guardrails layer for deterministic vehicle-safe answers, agent routing, and new tool integrations.

It also owns the vehicle backend interface contract: how the car talks to backend, across several vehicle generations at once.

• 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 (Lang Graph).
• 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:
  Open Telemetry distributed tracing, Lang Fuse 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…