Backend Engineer - LLM Integration

Our client is advancing its in-vehicle voice assistant into an intelligent, AI-powered companion. Since 2024, they have been incorporating large-language-model capabilities (Azure OpenAI / ChatGPT) into vehicles equipped with the MIB3 infotainment system, with new E³-architecture models featuring enhanced voice functions from the factory. The AIME (AI Model Engine) backend program supports this development over a multi-year timeline, addressing natural-language dialogue, empathic communication, and the complete cloud-edge data pipeline. DXC Luxoft serves as the end-to-end delivery partner, collaborating closely with the client’s engineers within a joint product team on the Azure platform (AKS, Azure OpenAI, Managed Identity, Azure Monitor, Azure DevOps)., Backend Engineers in this stream own the integration layer between the AI/ML components and the vehicle/cloud communication backbone. The stack is Python-first, event-driven via Kafka, and exposed through FastAPI services running on AKS. You will work inside the joint product team, picking up S/M/L backlog tickets and shipping production-ready code.

Build and maintain FastAPI microservices that orchestrate LLM calls, tool use, and agentic workflows using LangChain / LangGraph.

Implement Kafka producers and consumers for real-time voice session data, telemetry, and event-driven AI triggers.

Integrate with Azure OpenAI endpoints via LiteLLM abstraction; manage prompt versioning and A/B routing.

Write unit, integration, and contract tests (pytest, Pact); maintain coverage and quality gates in Azure DevOps.

Containerise services with Docker; deploy and operate on AKS; contribute to Helm chart and Terraform configuration.

Participate in code reviews with Audi engineers and contribute to joint architectural decisions.

Monitor service health using Azure Monitor / Application Insights; triage and resolve production incidents.

Document APIs (OpenAPI) and maintain ADR entries for significant implementation decisions.

Must have

3+ years professional Python development, including production web services.

Experience with FastAPI (or Flask/Django) for REST API design.

Familiarity with at least one LLM orchestration framework: LangChain, LangGraph, or LiteLLM.

Apache Kafka - producer/consumer implementation; at-least-once vs exactly-once semantics.

Docker and Kubernetes basics (deployment manifests, service configuration).

English B2 or above.