Technical Architect – AI & GenAI Specialization

About the role

We are looking for a Technical Architect specialized in AI and Generative AI to design and deliver enterprise‑grade AI solutions for Bosch’s global software division. The role will lead the creation of scalable, secure AI architectures that integrate with existing business systems.

Key responsibilities

• Define and implement AI architecture for enterprise applications, ensuring scalability, security, and compliance with data‑privacy regulations.
• Architect AI‑powered platforms that support generative AI, large language models, and machine‑learning frameworks.
• Execute training, fine‑tuning, and deployment of LLMs and ML models, applying techniques such as prompt engineering, retrieval‑augmented generation and knowledge distillation.
• Implement MLOps and AIOps best practices for continuous monitoring, improvement, and governance of AI models.
• Design and deploy AI solutions on cloud platforms (Azure AI, AWS SageMaker, Google Vertex AI, OpenAI), using containerization (Docker, Kubernetes) and cloud‑native patterns.
• Build data pipelines, feature stores, and vector databases to support efficient AI processing.
• Collaborate with engineering, data science, product and business teams, providing technical mentorship and communicating AI strategy to senior leadership.
• Ensure responsible AI practices, including bias mitigation, explainability, and protection against hallucinations and adversarial attacks.

Required profile

• B‑Tech/M‑Tech in Computer Science or related field.
• 10–12 years of experience in AI, Generative AI, large language models, machine learning and cloud AI services.
• Proven ability to lead technical teams and drive AI innovation in enterprise environments.
• Strong understanding of data‑privacy regulations such as GDPR and CCPA.

Required skills

• Artificial Intelligence (AI) and Generative AI
• Large Language Models (LLMs)
• Machine Learning (ML)
• Azure AI, AWS SageMaker, Google Vertex AI, OpenAI services
• Docker, Kubernetes
• MLOps and AIOps
• Prompt Engineering
• Retrieval‑Augmented Generation (RAG)
• Knowledge Distillation
• Data Pipelines, Feature Stores, Vector Databases