AI Edge Solution Architect – Edge/Cloud Voice AI

Luxoft Germany; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Luxoft Germany seeks a Technical Lead and Location Lead for the engineering team to own the architecture of hybrid cloud/edge voice assistant systems. You will drive end-to-end prototype pipelines (ASR → LLM → TTS), evaluate base components, and shape RAG architectures for automotive use cases while ensuring safety, security and interoperability across SOA and microservices.

You will lead an offshore team and coordinate with onshore architects.

Qualifications

• Edge/on-device AI optimisation (quantisation, pruning, distillation) for constrained hardware.
• Expert Python
• Hands-on with LLM frameworks in production: LangChain, LangGraph or equivalent.
• 3+ years as architect/technical lead with end-to-end system ownership.
• Track record of taking AI/ML/LLM systems from prototype to production.
• Practical experience building RAG systems: embedding models, vector stores, retrieval evaluation.
• Azure OpenAI Service or OpenAI API in practice, plus Azure cloud (AKS/Container Apps).
• Integration with at least one ASR or TTS engine (Whisper, Azure Speech, Cerence).
• Experience leading a distributed engineering team (5–10 engineers) in Scrum/Kanban.
• English C1, willingness to travel to Germany for workshops.

Responsibilities

• Own system architecture for hybrid (cloud/edge) voice assistant systems—define edge/cloud allocation and boundaries.
• Design and oversee end-to-end prototype pipelines (ASR → LLM → TTS) and test in lab and driving environments.
• Drive evaluation/benchmarking of base components (on-device ASR, TTS, LLM options) for latency, quality and energy.
• Define RAG architectures with embeddings and small vector stores for automotive use cases.
• Architect embedded/edge AI optimisation: quantisation, pruning, distillation for ARM/DSP/NPU with memory/energy focus.
• Shape the research agenda for on-device learning, multimodal interaction, privacy-preserving personalisation.
• Embed safety, security, privacy mechanisms and architecture interoperability (SOA, microservices).
• Lead offshore team as Location Lead: direction for AI/ML & AI Ops, code/design reviews, sprint planning.
• Coordinate with onshore architects and client teams; represent location in architecture boards and ceremonies.

Skills

Edge / on-device AI

Python

LangChain / LangGraph

Architect / technical lead

RAG systems

Azure OpenAI / OpenAI API

LLM production experience

Distributed team leadership

English: C1 + travel to Germany

Tools

LangChain

LangGraph

Azure OpenAI Service

OpenAI API

Whisper

Azure Speech

Cerence

ONNX Runtime

TensorRT

TFLite

OpenVINO

pyAudio

librosa

pydub

Job description

Project description

Our client is advancing its in-vehicle voice assistant into an intelligent, AI-powered companion. Large-language-model capabilities (Azure OpenAI / ChatGPT) have been running in production across vehicles. The goal of the project is to develop a backend which is the cloud AI orchestration service behind this: it receives requests from the vehicle, routes them, orchestrates the LLM, tool services and agents, and returns an answer or action to the car. DXC Luxoft serves as the end-to-end delivery partner, working in a joint product team with the client's engineers on the Azure platform.This is the series development and operations work package - a live platform serving a large vehicle fleet, which extends sprint by sprint the backend features while availability and backward compatibility are maintained. New capability in the pipeline includes streaming across the full ASR → LLM → TTS chain, barge-in, multi-intent handling, a guardrails layer for deterministic vehicle-safe answers, agent routing and new tool integrations.The role is Technical Lead and Location Lead for the engineering team. It is a hands-on delivery leadership role: the person is accountable for what the team ships, for the service running inside its availability and incident targets, and for the technical growth of the location.Work package A — Concept development & pre-development (research, evaluation, prototyping)

Responsibilities

• Own the system architecture for hybrid (cloud/edge) voice assistant systems — define the edge/cloud allocation model, routing criteria, and the port/adapter boundaries that allow functions to move between cloud and vehicle without redesign.
• Design and oversee end-to-end prototype pipelines (ASR → LLM → TTS), including integration into early vehicle platforms (E³, SDV) and test operation in lab and driving environments.
• Drive evaluation and benchmarking of base components: on-device ASR for edge hardware, modern TTS models (latency, robustness, audio quality, energy efficiency), and candidate LLMs for dialogue-based assistant functions via Azure AI Foundry.
• Define RAG architectures for improved knowledge coverage and robustness, including embedding strategies and small-scale vector stores for specific automotive use cases.
• Architect the embedded/edge AI optimisation workstream: quantisation, pruning and distillation for ARM/DSP/NPU targets, with explicit attention to memory footprint, energy consumption, wake-word efficiency and thermal behaviour.
• Shape the research agenda for future key technologies — continual learning and on-device model adaptation (anti-drift), multimodal interaction (speech + image + vehicle sensor data), emotion and sentiment recognition, privacy-compliant on-device personalisation and long-term memory, multi-speaker handling, speaker identification and anti-spoofing.
• Embed safety, security and privacy mechanisms into concepts from the start (differential privacy, secure enclaves, automotive security standards) and ensure interoperability with vehicle architectures (SOA, microservices, zonal architecture).
• Apply and enforce hexagonal architecture as the structural standard, so prototypes are transferable into series development rather than thrown away.
• Produce the technical concepts, architecture decision records and evaluation reports that hand research results over to the series development work package — and defend them in review with the client's architects.
• Lead the offshore engineering team as Location Lead: technical direction for AI/ML and AI Ops engineers, code and design review standards, work breakdown and estimation across a high-throughput ticket flow (~30 tickets/sprint, sizes S/M/L), onboarding and skills growth.
• Act as the offshore technical counterpart to the onshore solution architect and client engineering teams; run technical alignment across time zones and represent the location in architecture boards and sprint ceremonies.

SKILLS

Must have

• Edge / on-device AI: model optimisation (quantisation, pruning, distillation) and deployment to constrained hardware — ARM, DSP, NPU, or mobile/embedded equivalents.
• Expert Python.
• Hands-on with LLM frameworks in production: LangChain, LangGraph or equivalent.
• 3+ years as architect or technical lead, with end-to-end ownership of a system's design (within 8+ years total engineering experience).
• Track record of taking AI/ML or LLM systems from prototype into production — not research-only.
• Practical experience building RAG systems: embedding models, vector stores, retrieval evaluation.
• Azure OpenAI Service or OpenAI API in practice, plus Azure cloud (AKS or Container Apps).
• Integration experience with at least one ASR or TTS engine (Whisper, Azure Speech, Cerence or equivalent).
• Experience leading a distributed engineering team (5–10 engineers) in Scrum/Kanban.
• English C1, and willingness to travel to Germany for workshops, architecture reviews and prototype test campaigns.

Nice to have

• Hexagonal / ports-and-adapters architecture and API design (REST, gRPC, protobuf, OAuth2) — the client mandates this architecture style, so the candidate must be willing to adopt it quickly.
• Automotive / in-vehicle software context: infotainment platforms (MIB3, E³, SDV), Android Automotive / AAOS, AIDL, Viwi, vehicle telemetry services, SOA or zonal E/E architectures.
• In-vehicle speech processing specifics: noise cancellation, far-field microphones, wake-word engines, barge-in, multi-speaker cabin scenarios.
• Edge inference toolchains: ONNX Runtime, TensorRT, TFLite, OpenVINO; audio signal processing with pyAudio / librosa / pydub.
• Observability and evaluation for LLM systems: LangFuse, OpenTelemetry, and evaluation frameworks (DeepEval, Ragas, PromptFlow); quality metrics such as BLEU, WER, CER, faithfulness and hallucination rate.
• Commercial automotive speech/emotion stacks (Cerence, HumeAI) and Google TTS.
• Privacy-preserving ML: differential privacy, federated/aggregate learning signals, secure enclaves, on-device personalisation.
• Speaker identification and anti-spoofing.
• Data/persistence breadth: PostgreSQL + pgvector, MongoDB, CosmosDB; IaC with Terraform/Terragrunt.
• Awareness of automotive quality and security frameworks (A-SPICE, ISO/SAE 21434, TISAX) and of EU AI Act implications for AI systems.
• German language skills.