Project description

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).

Responsibilities

Design, implement, and optimize RAG pipelines integrating Azure AI Search, vector stores, and LLM completion endpoints.

Develop and maintain ASR and TTS integration modules, including audio pre

and post-processing using pyAudio and librosa.

Create LLM prompt chains, evaluation frameworks, and safety/alignment guardrails for in-vehicle dialogue scenarios.

Prototype AI capabilities such as few-shot adapters, intent classification, and empathic dialogue, then transition prototypes to production.

Collaborate with Backend Engineers to develop the FastAPI/Kafka interface connecting the AI layer with the streaming infrastructure.

Contribute to edge AI components including on-device inference, model quantization, and latency management for in-vehicle use.

Monitor and enhance model quality metrics including BLEU, WER, CER, and faithfulness via continuous evaluation pipelines.

Participate in code reviews with cross-functional teams, including customer ML engineers.

Skills

Must have

3+ years of hands-on AI/ML engineering in production environments.

Strong Python skills; experience with LLM frameworks: LangChain, LangGraph, or equivalent.

Practical experience building RAG systems (chunking strategies, embedding models, retrieval evaluation).

Familiarity with Azure OpenAI Service or OpenAI API; prompt engineering best practices.

Experience with at least one ASR engine (Whisper, Azure Speech, or equivalent) and a TTS system.

English B2 or above.

Nice to have

pyAudio / librosa audio signal processing.

Edge inference: ONNX, TensorRT, or on-device model deployment.

Automotive in-vehicle speech processing context (noise cancellation, far-field microphones).

Experience with LLM evaluation frameworks (DeepEval, Ragas, PromptFlow).

German language skills.

Other

Languages

English: B2 Upper Intermediate

Seniority

Senior

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