Equipment Data Foundation Model Specialist

Job Description Summary

We are seeking a senior technical leader with deep expertise in Large Language Models, Applied Machine Learning, and advanced analytics. In this role, you will lead the development of customized LLMs using IoMT and related datasets, and architect intelligent reasoning systems that power next generation digital capabilities.

You will guide the end to end development of AI solutions—from problem framing and data strategy through modeling, fine tuning, evaluation, and deployment in partnership with product, engineering teams and principal architects. As a senior technical leader, you will influence architecture decisions, elevate analytical rigor, mentor teams, and ensure our AI solutions deliver meaningful, measurable impact.

Job Description

Key Responsibilities

1. Thought Leadership in Applied Data Science & Analytics

• Define analytical strategies that address high‑value business problems and align with enterprise priorities.

• Lead end‑to‑end data science solution design, including data preparation, feature development, model selection, and evaluation.

• Apply advanced statistical, ML, NLP, and optimization methods to build robust, high‑impact models.

• Establish best practices around model rigor, experimentation frameworks, and interpretability.

• Deliver insights, algorithms, and intelligence that drive business decision‑making and innovation.

2. LLM Architecture, Training & Advanced Methodologies

• Serve as the subject‑matter expert for LLM development, fine‑tuning, and applied research.

• Lead experimentation on novel training methods, prompting approaches, data curation strategies, and evaluation techniques.

• Design and refine custom LLMs, including work on instruction tuning, retrieval‑augmented generation (RAG), and reasoning frameworks.

• Develop agentic and tool‑use capabilities, including digital agents, workflow orchestration, and structured reasoning.

• Define reusable patterns and technical standards for LLM‑based solutions across the organization.

• Evaluate GenAI technologies, frameworks, and model providers; guide build‑versus‑buy recommendations.

• Ensure LLM development aligns with Responsible AI, security, and compliance principles.

3. Model Delivery & Partnership with Engineering

• Collaborate with ML engineering teams to operationalize models and ensure reliable deployment.

• Provide technical guidance on model evaluation, inference strategies, and integration patterns.

• Offer expertise during design reviews, architecture discussions, and solution planning.

• Ensure final solutions are scalable, maintainable, and aligned with business needs—without owning infrastructure or cluster management.

4. Strategic Influence, Cross‑Functional Leadership & Communication

• Partner with product, engineering, and business leaders to define AI roadmaps and prioritize high‑impact opportunities.

• Shape solution architectures and analytical approaches across multiple initiatives and teams.

• Translate complex technical concepts into clear recommendations for leadership and stakeholders.

• Guide teams through end‑to‑end delivery of AI use cases, from ideation to production adoption.

5. Mentorship, Capability Building & Knowledge Sharing

• Mentor data scientists, analysts, and AI practitioners across teams.

• Lead internal learning initiatives, workshops, and communities of practice.

• Establish high technical standards and champion a culture of innovation, rigor, and humility.

• Stay ahead of advancements in LLMs, multimodal AI, reasoning models, IR, and AI safety—and help integrate them into our roadmap.

Qualifications

Must‑Have Qualifications

• Extensive experience solving complex business problems using ML, NLP, and advanced analytics.

• Deep hands‑on expertise in LLM fine‑tuning, evaluation, prompt engineering, and GenAI development.

• Skilled with LLM toolkits and platforms (Hugging Face, LangChain, AWS Bedrock/SageMaker).

• Strong foundation in statistics, ML theory, NLP, optimization, and modeling best practices.

• Proven track record designing and delivering AI solutions end‑to‑end in enterprise environments.

• Proficiency in Python; familiarity with distributed data tools (Spark, Ray) is beneficial.

• Understanding of LLM architectures, model optimization strategies, and GPU‑based training concepts (at a conceptual, not infra‑admin level).

• Experience with modern LLM techniques (PEFT, prompt optimization, RL, Chain‑of‑Thought).

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or related discipline.

• 6+ years developing and deploying advanced analytics or AI solutions on major cloud platforms.

• 6+ years deep learning experience with a focus on NLP and generative models.

• Experience collaborating with AI-native cloud platforms (OpenAI, Anthropic, AWS, Azure, Google Cloud).

• Background in agent-based systems, reasoning models, or reinforcement learning is a plus.

• Strong understanding of modern LLM advancements (long-context models, multimodal systems, alignment techniques, agent ecosystems).

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: 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.

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Additional Information