AI Software Architect

Project description

DXC-Luxoft is an industry-leading software integrator and solution house for automotive OEMs and suppliers. There are millions of cars on the road today with solutions designed by DXC-Luxoft. DXC-Luxoft is currently supporting several German OEMs in building their next-generation battery-electric vehicle platforms. We help industrializing, integrating and testing the platform and functional software, especially during critical integration phases approaching start-of-production. For expanding our business offering in the European market, we seek to establish experienced automotive engineers. DXC-Luxoft is happy to receive applications from self-motivated and self-disciplined applicants who communicate well, like to work in teams, and are highly motivated to bring value to our automotive customers. For the successful candidate, we offer the opportunity to enhance and develop your career with the following benefits:

-Join a highly competent and motivated international team

- Apply your talent to creating the mobility solutions of the future

- Gain hands-on experience working with well-recognized customers

- Personal growth and promotion options

- Make an impact on the overall growth of DXC-Luxoft

- Our interesting project connected with cutting-edge automotive technologies.

Responsibilities

• - Lead AI and GenAI initiatives across customer projects by providing architecture guidance and technical leadership.

  - Develop a deep understanding of business challenges and identify opportunities for AI-driven automation and productivity improvements.

  - Design and implement enterprise-grade AI solutions, including LLM-powered applications, intelligent assistants, RAG systems and agentic/multi-agent workflows, deployed on lakehouse/data platforms (e.g. Databricks).

  - Design tool-calling interfaces for AI agents (e.g. MCP-style tool servers) that expose internal data, diagnostics and business logic safely to LLM-based systems.

  - Define architecture, technology stack, security controls and governance principles for AI-powered platforms, including role-based access, data-usage governance and time-boxed permission models.

  - Establish structured release and promotion practices for AI platforms (e.g. DEV → PRE/staging → PROD gates) with monitoring, rollback and production-verification discipline.

  - Support pre-sales activities by creating solution concepts, architecture proposals, estimates and technical presentations.

  - Drive adoption of modern AI engineering practices, including MLOps, LLMOps, evaluation frameworks, observability and Responsible AI principles.

  - Collaborate with stakeholders, product owners and engineering teams to translate business requirements into scalable technical solutions.

SKILLS

Must have

• - 7+ years of professional experience - development / architecture with proven experience as SW Architect, Solution Architect or AI Architect.

  - Strong expertise in designing and delivering enterprise AI and GenAI solutions.

  - Hands-on experience with LLMs and AI frameworks (OpenAI, Azure OpenAI, LangChain, Semantic Kernel, AutoGen, CrewAI or similar).

  - Strong knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, prompt engineering and agentic architectures.

  - Experience with lakehouse/data-platform ecosystems (e.g. Databricks) for hosting and serving AI applications, including CI/CD-driven deployment via GitHub Actions or similar.

  - Understanding of AI governance, security, privacy and Responsible AI practices, including role-based and time-boxed data-access models.

  - Experience establishing CI/CD pipelines and MLOps/LLMOps processes for AI solutions.

  - Experience producing architecture documentation and technical design artifacts.

  - Eager to learn, with strong communication, collaboration and stakeholder management skills.

  - Strong focus on business outcomes and ability to solve complex customer problems using AI technologies.

Nice to have

- Experience in Automotive engineering projects.

- Experience with Azure AI Foundry, Azure Machine Learning and Azure AI Services.

- Experience implementing multi-agent and autonomous AI systems.

- Experience with Neo4j, GraphRAG or knowledge graph solutions.

- Familiarity with vector databases such as Pinecone, Weaviate, Azure AI Search, Qdrant or Milvus.

- Experience supporting presales activities, customer workshops and solution discovery sessions.

- Ability to write clean, maintainable and testable code.