Senior Solution Architect

Job Description

About the Team

Our team builds enterprise-grade AI platforms and solutions across Agentic AI, Generative AI for the software development lifecycle, systems engineering, automated testing, MLOps, computer vision, and engineering data intelligence.

We are looking for architects who can translate complex automotive and engineering challenges into scalable AI, cloud, and software architecture, and help take solutions from research and proof of concept to production across global engineering teams.

What you can look forward to as Solution Architect: AI/ML (m/f/d)

• End-to-end ownership: You'll own the technical architecture of enterprise Generative AI and Agentic AI solutions, from framing the business problem through to secure, observable and cost-efficient production systems.
• Real-world impact: You'll turn LLM capabilities into reliable, auditable solutions that hold up against complex enterprise data, strict security and compliance requirements, tight latency and cost budgets, and unpredictable model behavior. This role goes well beyond proof of concept.
• Architecture leadership: You'll set the reference architecture, platform patterns and guardrails that multiple engineering teams build on.
• Strategic decisions: You'll lead decisions on what to build versus buy, which models to use, and whether to customize models through fine-tuning, weighing quality, cost, scalability and vendor independence.
• Cutting-edge technology: You'll design multi-agent systems on leading frameworks and cloud platforms, such as LangGraph, AWS Bedrock and Azure AI Foundry.
• Visibility and influence: You'll be the trusted technical authority for business leaders, security teams and enterprise architecture boards.

Your Profile as Solution Architect: AI/ML (m/f/d)

• A bachelor's or master's degree in computer science, Engineering, AI or a related field.
• 10+ years in software, data or ML engineering, including at least 3 years as a solution or lead architect.
• Production GenAI delivery: You have delivered at least 2-3 LLM / Generative AI solutions into production, not only proofs of concept, and owned the architecture and non-functional requirements end to end.
• Agentic AI: You have built hands-on agentic systems hands-on, with tool use, state management and human-in-the-loop controls. You've used code-first frameworks such as LangGraph / LangChain or managed platforms such as AWS Bedrock Agents or Azure AI Foundry Agent Service.
• LLM and model expertise: You have a strong grasp of LLM fundamentals (transformers, embeddings, context windows). You have hands-on experience customising models through fine-tuning (e.g., LoRA / QLoRA) and evaluating them, and you can choose sensibly between prompting, RAG and training.
• Engineering and cloud: You are an expert in Python and API engineering (FastAPI, async, event-driven design). You have strong architecture experience on at least one hyperscaler (AWS, Azure or GCP), including networking, identity and security.
• RAG, quality and security: You have practical experience with vector databases, hybrid search and retrieval evaluation. You've implemented LLM evaluation, tracing and observability in production, and you're well-grounded in AI security (prompt-injection defence, data protection).