AI Architect

Let's introduce ourselves

At Volvo Cars, we are making bold digital visions come true. We aim to be the leader in the automotive world by creating a digital ecosystem built around making our customers' lives less complicated. What we all have in common at Volvo Cars is our passion for protecting lives, our endless curiosity and our dedication to create a new future for the automotive industry. Our human centric focus is what separates us from all other car companies.

India Digital Tech Hub – Bangalore

The Digital Tech Hub is an integral part of the digital transformation journey for Volvo Cars. The Tech Hub is a strategic player, providing skills and competencies in areas such as cloud solutions, data science and analytics, SAP, UX/UI and we are part of managing key technology platforms within various domains such as R&D, manufacturing & logistics and the commercial area.

What you'll do

The AI Architect defines and governs the semantic and conceptual foundations that make AI systems coherent, interoperable, and reusable across the organization. The role is accountable for how meaning is represented to ensure that agents, models and retrieval systems operate on shared concepts rather than isolated data sets.

The role connects business intent and technical execution and thus translates domain concepts into semantic models, knowledge structures and architectural patterns that AI systems can reliably reason over cross the organization.  

The AI Architect defines and governs the semantic and conceptual foundations that make AI systems coherent, interoperable, and reusable across the organization. The role is accountable for how meaning is represented to ensure that agents, models and retrieval systems operate on shared concepts rather than isolated data sets.

The role connects business intent and technical execution and thus translates domain concepts into semantic models, knowledge structures and architectural patterns that AI systems can reliably reason over cross the organization.  

Key responsibilities

Semantic & AI Architecture

• Define enterprise-wide semantic architecture for AI, including ontologies, conceptual models, vocabularies and knowledge abstraction in collaboration with information architecture team to underpin AI and GenAI solutions.
• Create and refine reference architecture and design patterns for semantic layers, knowledge graphs and embedding strategies.
• Architect end-to-end AI solutions with separation of concerns between data, semantics, modes, and orchestration.
• Ensure consistent representation of entities, relationships, and business concepts across embeddings, vector stores, LLM prompts and agent memory.

Knowledge, retrieval & reasoning layers

• Design and govern knowledge layers that combine structure data, unstructured data and domain semantics.
• Define standards for semantic search and context assembly.
• Ensure semantic models remain evolvable, versioned, and decoupled from specific LLM vendors or tools.

Collaboration and use-case realisation

• Work with business stakeholders to formalise use cases as semantic problems (concepts, decisions, relationships), not just applications or features.
• Collaborate with data scientists and engineers to ensure AI solutions are grounded in shared semantic models.
• Ensure solutions align with policies, security, auditability and regulations.
• Challenge use cases that bypass semantic modelling in favour of prompt-based or point-solution design.
• Evaluate and introduce AI, knowledge, and semantic technologies that strengthen long-term interoperability and reuse across the organization.

What you'll bring

• 7+ years designing and delivering production AI systems, with a strong foundation in software, data or cloud architecture. 
• Hands-on experience with LLMs, semantics search, vector databases and RAG architectures.
• Proven experience designing semantic layers: knowledge graphs, ontologies, metadata models and embeddings. 
• Experience operating AI systems at scale in enterprise or regulated environments, using platforms such as Microsoft AI Foundry, Amazon Bedrock, Snowflake Cortex, or Databricks AI. 
• Strong MLOps and platform engineering experience (e.g., Azure ML, SageMaker, MLflow, Kubeflow and containerized environments (e.g. Docker, Kubernetes). 
• Familiarity with AI ethics, bias, transparency, data privacy, and regulatory requirements.