Solution Architect – LLM Backend & Vehicle Interface

About the Position

DXC Luxoft is seeking a Solution Architect – LLM Backend & Vehicle Interface to design and maintain a cloud AI orchestration service for an intelligent in-vehicle voice assistant. This role is pivotal in advancing our client's in-vehicle voice assistant into an AI-powered companion, leveraging large-language-model capabilities (Azure OpenAI / ChatGPT) running in production across vehicles with new E³-architecture models.

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, and clean cancellation/interruption semantics (the foundation for barge-in).
• Own the LLM integration layer: prompt normalization 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: deciding 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, 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.

Requirements

• Must have

  • 8+ years in backend/distributed systems engineering, including 3+ years as architect or technical lead with end-to-end ownership of a production system's design.
  • Expert Python: FastAPI, async, and hands-on experience with 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.

• Nice to have

  • Automotive or in-vehicle software background: MIB3 / E³ / SDV platforms, Android Automotive (AAOS) and AIDL interfaces, Viwi protocol, vehicle telemetry and configuration services, SOA / microservice / zonal E/E architectures.
  • Experience with LLM evaluation and prompt regression practice: LangFuse, DeepEval, Ragas, PromptFlow; metrics such as faithfulness, hallucination rate, latency P95, WER/CER.
  • ASR/TTS service integration at the streaming level (Azure Speech, Whisper, Cerence, Google TTS) and awareness of in-cabin speech conditions (noise, far-field microphones, multi-speaker).
  • Emotion/sentiment APIs (HumeAI) and empathic dialogue design.
  • Broader persistence experience: MongoDB (pymongo/beanie), CosmosDB, and a clear opinion on when each belongs in the picture.
  • Semantic caching and token-cost optimization at fleet scale.
  • Awareness of automotive quality, security, and AI governance frameworks: A-SPICE, ISO/SAE 21434, TISAX, GDPR/privacy-by-design, EU AI Act implications.
  • Experience taking over and stabilising a running system from a previous supplier (knowledge transfer, reverse-documenting an inherited codebase).

We Offer

• Competitive salary
• Remote work options
• Professional development budget

About the Company

DXC Luxoft is a leading provider of technology services, specializing in cloud, data, and AI. We partner with clients to deliver innovative solutions that drive business transformation and growth.