About Architect:

The AI Architect will drive the design, development, and integration of AI capabilities across the Qorix Developer Platform. This role ensures that GenAI, agentic systems, and ML-driven workflows seamlessly extend our tooling ecosystem built around EMF, GraphQL, VS Code extensions, and configuration authoring for automotive middleware.

Come join us & innovate next-gen automotive and business IT solutions for next-gen vehicles with Qorix.

Key Responsibilities Include:

AI Architecture & System Design

 

  • Architect scalable, resilient, and secure AI systems integrated directly into developer tooling workflows.
  • Own the end-to-end lifecycle of AI features — design, experimentation, evaluation, deployment, and optimization.
  • Define architectural building blocks for GenAI-based assistants, RAG pipelines, MCP clients/servers, and agentic workflows.
  • Build reusable AI components for IDE extensions, EMF-based models, code generators, and configuration pipelines.

 

Generative AI & Agentic Systems

 

  • Lead the design and implementation of LLM-powered features using GPT, Claude, Llama, or custom fine-tuned models.
  • Build RAG pipelines grounded in AUTOSAR and Non-AUTOSAR SWS/TPS, ARXML schemas, EMF metamodels, and domain-specific knowledge graphs.
  • Implement agentic AI using frameworks such as LangChain, LangGraph, LlamaIndex, and model context protocols (MCP).
  • Apply advanced prompt engineering, tool-based prompting, and structured output strategies for deterministic behaviour.

 

Deep Learning & ML Engineering

 

  • Apply strong   foundational   knowledge   in    ML    algorithms, neural networks, embeddings, transfer learning, and evaluation
  • Use PyTorch, TensorFlow, Keras, and   Hugging Face ecosystems (Transformers, PEFT, LoRA) to build and optimize models.
  • Design semantic embeddings for specification documents, ARXML models, and configuration metadata.

 

MLOps & DevOps

 

  • Implement end-to-end ML pipelines for training, validation, inference, monitoring, and continuous improvement.
  • Deploy and scale AI workloads using containerization (Docker) and orchestration (Kubernetes).
  • Utilize MLflow, Kubeflow, or equivalent for experiment tracking, model registry, and lifecycle automation.
  • Build production-grade guardrails, monitoring, traceability, and observability for AI systems.

 

Data Architecture

 

  • Design data ingestion and processing pipelines for unstructured docs, ARXML, EMF models, logs, user telemetry, and code assets.
  • Operate vector stores (Milvus, Chroma, Pinecone) and graph databases (Neo4J or similar) for semantic retrieval and reasoning.
  • Ensure robust data governance, schema alignment, metadata management, and feature engineering.

 

Cloud & Enterprise Integration

 

  • Deploy AI services across AWS, Azure, or GCP and align with enterprise
  • Utilize cloud-native AI services such as AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
  • Integrate AI components with APIs, microservices, and the larger tooling platform.

 

Security & Compliance

 

  • Ensure alignment with enterprise-grade security practices (OAuth2, JWT, TLS hardening, secrets management).
  • Implement privacy, governance, GDPR, and functional safety–aware design where required.

Skills / Competencies:

Technical

 

  • Strong system-design thinking with an ability to balance latency, throughput, cost, and reliability.
  • Deep understanding of model architectures, embeddings, fine-tuning, and evaluation.
  • Expertise in Python and hands-on with modern AI
  • Ability to design and integrate AI solutions inside IDEs and configuration tooling (VS Code, Eclipse RCP, custom editors).
  • Experience grounding AI models in domain-heavy systems (AUTOSAR, middleware stacks, IDLs, schemas).

 

Behavioural

 

  • Strategic thinker  who  aligns  AI  roadmap with product and organizational goals.
  • Excellent communication skills — able to translate deep AI concepts for engineering, product, and leadership.
  • Highly collaborative, hands-on, and comfortable working in fast-paced.
  • Curiosity-driven, self-learning mindset with a passion for adopting cutting-edge AI technologies.
  • Customer-centric outlook with a focus on user  experience, determinism, and reliability.

Experience:

10 to 15 years

Relevant Exp:

  • 5+ years in AI/ML, Automotive domain will be added advantage

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