AI/ML Engineer – LLM & Voice Pipeline

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.