Principal AI Architect

GE Healthcare GE Healthcare · Healthcare · Bengaluru, Karnātaka, India · Digital Technology / IT

The Principal Architect – AI & Equipment Data Management defines and governs the enterprise architecture for AI-powered equipment intelligence platforms, focusing on the Equipment Intelligence 2.0 transformation. This involves scalable data platforms, fine-tuned LLMs, RAG, and agentic AI capabilities. The role provides architectural leadership across cloud, AI, and data, ensuring alignment with security, privacy, compliance, and AI governance standards.

What you'd actually do

  1. Define and own the end-to-end architecture for equipment data platforms across device, cloud, data, and AI layers.
  2. Establish architectural standards, reusable patterns, and governance models for scalability, reliability, security, and compliance.
  3. Lead the architecture strategy for Equipment Intelligence 2.0, including: Fine-tuned LLM capabilities, Retrieval-Augmented Generation (RAG) intelligence layers, Agentic AI and workflow orchestration layers, AI-driven reasoning and decision-support systems
  4. Define scalable reference architectures and reusable frameworks for GenAI-enabled equipment intelligence solutions.
  5. Guide engineering and ML teams in implementing AI/ML use cases such as predictive maintenance, anomaly detection, optimization, and automation.

Skills

Required

  • AWS Native services - S3, Cloud watch, step function, lambda, spark, Glue python shell, Sage Maker, Elastic Search & Kibana reports, Athena, redshift, RDS
  • Big Data and Cloud (AWS) eco-systems, in developing, designing of AI/ML products
  • dealing with newer/ less matured technologies, proof-of-concepts, driving pilots, and product evaluations
  • explore alternate technology and approaches to solving problems
  • breaking down problems, documenting problem statements and estimating efforts
  • analyze impact of technology choices
  • negotiation to align stakeholders and communicate a single synthesized perspective to the scrum team and balancing value propositions for competing stakeholders
  • Strong understanding of AI governance, security, privacy, and Responsible AI principles.
  • Proven leadership in enterprise architecture, stakeholder management, and strategic technology decision-making
  • Bachelor’s Degree in Computer Science or in STEM” Majors (Science, Technology, Engineering and Math) or equivalent experience
  • Minimum of 12+ years of IT professional experience or equivalent of experience in Software Architecture or IT
  • Minimum 3+ years AWS cloud experience

Nice to have

  • AWS certifications (preferable AWS Solution Architect)

What the JD emphasized

  • architectural standards
  • governance models
  • scalability
  • reliability
  • security
  • compliance
  • Responsible AI
  • privacy
  • cybersecurity
  • enterprise governance standards
  • AI governance
  • security
  • privacy
  • Responsible AI principles

Other signals

  • defining and governing the enterprise architecture for AI-powered equipment intelligence platforms
  • enabling the Equipment Intelligence 2.0 transformation through scalable data platforms, fine-tuned LLMs, RAG-based intelligence layers, and agentic AI capabilities
  • architectural leadership across cloud, AI, and data platforms
  • architectural standards, reusable patterns, and governance models for scalability, reliability, security, and compliance
  • architecture strategy for Equipment Intelligence 2.0, including: Fine-tuned LLM capabilities, Retrieval-Augmented Generation (RAG) intelligence layers, Agentic AI and workflow orchestration layers, AI-driven reasoning and decision-support systems
  • scalable reference architectures and reusable frameworks for GenAI-enabled equipment intelligence solutions
  • architecture decisions for AI platforms, vector databases, orchestration frameworks, model integration, and inference patterns
  • Responsible AI, privacy, cybersecurity, and enterprise governance standards
  • technical standards and governance for enterprise adoption of LLM and agentic AI solutions
  • architectural guidance for model deployment, integration patterns, evaluation strategies, and operational scalability
  • operationalize AI solutions within enterprise platforms
  • AI and data roadmaps
  • enterprise technology strategy and drive adoption of modern AI and data capabilities