Senior LLM Backend Architect for Vehicle Interfaces

Luxoft Germany; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Luxoft Germany is seeking a senior backend architect to own the architecture of a production backend supporting in-vehicle voice assistant services. You will drive end-to-end design for streaming AI, LLM orchestration, and tool/agent routing across vehicle generations.

The role requires deep expertise in Python, streaming tech, and cloud-native architectures on Azure. Responsibilities include defining interface contracts, REST/gRPC integrations, security, and high-availability targets.

Qualifications

• 8+ years in backend/distributed systems engineering, including 3+ years as architect or technical lead with end-to-end ownership.
• Expert Python: FastAPI, async, streaming architectures (SSE/WebSocket/gRPC streaming, back pressure, cancellation).
• Proven experience architecting LLM-based production systems — orchestration of tool- and agent-based workflows with LangChain / LangGraph or equivalent, prompt strategies, structured outputs, LLM constraint handling and guard-railing.
• Azure OpenAI Service or OpenAI API in production, including model version migration and the compatibility problems it creates.
• Strong API and interface design: REST and gRPC/protobuf, versioning and backward compatibility strategies, OAuth2 and mTLS for secure service-to-service communication.
• PostgreSQL at depth: schema design, tuning, migrations (Alembic); plus working experience with embeddings and vector search for RAG (pgvector or equivalent).
• Kubernetes / container orchestration (deployment, scaling, secrets/config, networking, load balancing) and Azure cloud architecture (AKS or Container Apps, Managed Identity, Key Vault, Monitor).
• Infrastructure as Code with Terraform: reusable modules, environment separation, remote state and locking, CI/CD integration, lifecycle management.
• Demonstrable responsibility for running a production service: monitoring, alerting, distributed tracing with OpenTelemetry, incident and problem management, patch and release management, latency and cost optimization against defined targets.

Responsibilities

• 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.
• 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.
• 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.
• Make the architecture hold its operational targets: high availability in the 99.5% SLO range, incident resolution inside 24 hours, 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.

Skills

Backend engineering

Distributed systems

Python (FastAPI)

Streaming architectures

LLM orchestration

Azure OpenAI

API design

Kubernetes

Terraform

OpenTelemetry

Tools

PostgreSQL

pgvector

AKS

Terraform

OpenTelemetry

Azure DevOps

Job description

Luxoft Germany is seeking a senior backend architect to own the architecture of a production backend supporting in-vehicle voice assistant services. You will drive end-to-end design for streaming AI, LLM orchestration, and tool/agent routing across vehicle generations.

The role requires deep expertise in Python, streaming tech, and cloud-native architectures on Azure. Responsibilities include defining interface contracts, REST/gRPC integrations, security, and high-availability targets.