Key Responsibilities
1. Enterprise AI & Equipment Data Architecture
Define and own the end-to-end architecture for equipment data platforms across device, cloud, data, and AI layers.
Establish architectural standards, reusable patterns, and governance models for scalability, reliability, security, and compliance.
Design and guide real-time and batch data ingestion, streaming, storage, and analytics architectures.
Partner with product, engineering, and business leaders to align architecture with strategic business outcomes.
Evaluate emerging AI, IoT, and cloud technologies to influence long-term platform strategy.
2. Equipment Intelligence 2.0 Transformation
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
Define scalable reference architectures and reusable frameworks for GenAI-enabled equipment intelligence solutions.
Guide architecture decisions for AI platforms, vector databases, orchestration frameworks, model integration, and inference patterns.
Ensure architecture designs align with Responsible AI, privacy, cybersecurity, and enterprise governance standards.
Drive architecture reviews and secure approvals from enterprise Privacy, Security, and AI Technology Councils.
Define technical standards and governance for enterprise adoption of LLM and agentic AI solutions.
Development of a roadmap, design pattern, and implementation based upon a current vs. future state in a cohesive architecture viewpoint.
Coordinate Technical Design Reviews (Concept, Development, and Evaluation TDR’s).
3. AI Solution Enablement & Delivery Guidance
Guide engineering and ML teams in implementing AI/ML use cases such as predictive maintenance, anomaly detection, optimization, and automation.
Provide architectural guidance for model deployment, integration patterns, evaluation strategies, and operational scalability.
Collaborate with engineering teams to operationalize AI solutions within enterprise platforms.
Ensure solutions are maintainable, scalable, and aligned with business and operational requirements.
Support architecture reviews, technical decision-making, and solution planning across delivery programs.
4. Strategic Leadership & Stakeholder Management
Partner with business, product, and technology leaders to define AI and data roadmaps.
Translate complex technical concepts into clear recommendations for executive stakeholders.
Provide architectural governance and technical leadership across multiple programs and teams.
Influence enterprise technology strategy and drive adoption of modern AI and data capabilities.
5. Technology Modernization & Continuous Improvement
Identify opportunities for platform modernization, architecture refactoring, and optimization.
Improve key architecture KPIs including scalability, throughput, cost efficiency, and delivery cycle time.
Guide engineering teams to implement improvements safely and effectively.
Evaluate trade-offs and influence strategic technology decisions through data-driven recommendations.
Core Qualifications
Working experience with AWS Native services - S3, Cloud watch, step function, lambda, spark, Glue python shell, Sage Maker, Elastic Search & Kibana reports, Athena, redshift, RDS
Strong knowledge of Big Data and Cloud (AWS) eco-systems, in developing, designing of AI/ML products
Experience with dealing with newer/ less matured technologies, proof-of-concepts, driving pilots, and product evaluations.
Demonstrates the initiative to explore alternate technology and approaches to solving problems
Skilled in breaking down problems, documenting problem statements and estimating efforts
Has the ability to analyze impact of technology choices
Skilled in 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
Education Qualification
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
AWS certifications (preferable AWS Solution Architect).
Inclusion and Diversity
GE Healthcare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
We expect all employees to live and breathe our behaviors: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership – always with unyielding integrity.
Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything you’d expect from an organization with global strength and scale, and you’ll be surrounded by career opportunities in a culture that fosters care, collaboration and support.
Relocation Assistance Provided: No