Key Responsibilities
Conduct advanced research in artificial intelligence, with focus areas including machine learning, deep learning, generative AI, large language models, natural language processing, GANs, multimodal AI, and agentic AI systems.
Design, prototype, and validate novel AI algorithms, architectures, and workflows for real-world use cases.
Explore and apply cutting-edge approaches in transformers, fine-tuning, retrieval-augmented generation (RAG), prompt optimization, autonomous agents, multi-agent systems, model alignment, and reasoning frameworks.
Lead experimentation across model training, evaluation, benchmarking, and optimization.
Stay current with emerging AI advances and translate academic research and industry innovation into scalable enterprise solutions.
Publish research findings, contribute to patents, or create internal technical thought leadership that advances the organization’s AI maturity.
Build, fine-tune, and optimize ML/DL models, including supervised, unsupervised, reinforcement, and self-supervised learning systems.
Develop and deploy LLM-powered applications, conversational AI, summarization systems, semantic search, knowledge assistants, and intelligent automation platforms.
Create Generative AI applications using foundation models for text, image, code, synthetic data, and multimodal outputs.
Design and implement GAN-based solutions for synthetic data generation, image synthesis, anomaly simulation, data augmentation, and domain-specific generative use cases.
Develop Agentic AI systems capable of task planning, tool usage, workflow orchestration, memory integration, retrieval, and decision support.
Use AWS Bedrock to build and scale foundation model applications, including model access, orchestration, secure integration, and GenAI experimentation.
Use AWS SageMaker for model training, tuning, experimentation, MLOps, deployment, and monitoring at scale.
Work with structured and unstructured data across large-scale datasets to support AI research and production systems.
Lead or collaborate on data cleaning, feature engineering, data quality improvement, dataset curation, and annotation strategies.
Build robust AI pipelines that integrate with enterprise data systems, APIs, cloud services, and downstream applications.
Apply SQL, NoSQL, database modeling, and data warehousing concepts to support efficient model training and inference.
Partner with engineering teams to productionize models with scalability, observability, reliability, and security in mind.
Ensure all AI systems are designed and deployed with Responsible AI principles.
Develop practices for fairness, transparency, interpretability, explainability, privacy, accountability, and bias mitigation.
Assess risks associated with foundation models, LLM outputs, hallucinations, model drift, adversarial misuse, and unsafe automation.
Implement guardrails, evaluation standards, governance frameworks, and human-in-the-loop processes where necessary.
Support compliance with evolving data privacy, security, and ethical AI requirements.
Translate complex AI concepts into clear business value propositions for stakeholders, leadership teams, and non-technical audiences.
Collaborate with product, engineering, security, legal, data, and business teams to define AI strategy and deliver measurable outcomes.
Mentor junior scientists, ML engineers, and data professionals.
Contribute to roadmap planning, architecture reviews, technical hiring, and AI capability development across the organization.
Required Qualifications
PhD or Masters in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, or a related quantitative discipline with a Minimum of 6+ years of experience.
Research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work.
Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, Agentic AI / AI Agents
Proven experience developing advanced AI models from research through implementation and evaluation.
Good experience with AWS Bedrock and AWS SageMaker for foundation model development, model lifecycle management, and deployment workflows.
Good understanding of Responsible AI, including model governance, fairness, explainability, privacy, bias mitigation, and risk control.
Core Technical Skills
Expert-level proficiency in Python as the top priority language for AI and ML development.
Ability to build efficient, scalable, and production-ready code for research and enterprise AI applications.
Good understanding of core ML concepts, including Transformer architectures
Hands-on experience with leading frameworks such as PyTorch, TensorFlow, Keras
Experience with model selection, hyperparameter tuning, training optimization, evaluation metrics, model compression, and inference performance improvement.
Expertise in NLP techniques, including text classification, NER, embeddings, summarization, semantic retrieval, question answering, sentiment analysis, and conversational AI.
Experience building LLM applications, including prompt engineering, fine-tuning, RAG pipelines, evaluation, grounding, and safety controls.
Expertise in Generative AI architectures, foundation models, and enterprise use cases involving text, image, document, and multimodal generation.
Good experience with building AI agents and autonomous workflows.
Skills in, Agent architecture and orchestration, Tool use and function calling, Retrieval systems, Memory design, Reliability engineering, Evaluation and guardrails, Multi-step planning and execution
Familiarity with modern agent frameworks and orchestration patterns for enterprise-grade agentic systems.
Experience in Data cleaning and preprocessing, Feature engineering, SQL and database querying, Database modeling, NoSQL systems, Data warehousing, Large-scale data handling
Ability to work with diverse datasets and establish data foundations for AI systems.
Ability to apply mathematical reasoning to model design, tuning, experimentation, and performance analysis.
Good experience with AWS Bedrock, AWS SageMaker, AWS data and ML services relevant to AI model development and deployment
Familiarity with cloud-native AI system design, scalable training, model serving, monitoring, and MLOps practices.
Commitment to designing fair, accountable, transparent, and human-centered AI systems.
Ability to identify, assess, and mitigate ethical risks in model design, training data, inference, and deployment.
Expertise in crafting, testing, and optimizing prompts for foundation models and LLM-driven applications.
Ability to design prompt strategies that improve relevance, reliability, task completion, and output quality.
Skill in translating domain challenges into AI opportunities and practical solutions.
Ability to solve complex, ambiguous, and open-ended AI problems.
Comfortable navigating evolving requirements, incomplete data, experimental uncertainty, and rapid technological change.
Excellent verbal and written communication skills.
Ability to explain technical concepts, model limitations, trade-offs, and business implications to both technical and non-technical stakeholders.
Good collaboration skills across research, engineering, product, and leadership teams.
Curiosity and commitment to ongoing learning in a rapidly evolving AI landscape.
Ability to evaluate new tools, methods, and research directions and determine where they create business value.
Preferred Qualifications
Postdoctoral research, industrial research lab experience, or significant applied research leadership in AI.
Excellent publication record in reputable AI/ML/NLP conferences or journals.
Experience with multimodal AI, including text, image, audio, video, or document intelligence systems.
Experience with RAG pipelines, vector databases, tool-using agents, and advanced LLM evaluation frameworks.
Familiarity with MLOps, CI/CD for ML, model monitoring, A/B testing, and production observability.
Knowledge of privacy-preserving AI techniques, model security, red teaming, and governance workflows.
Experience leading AI innovation programs or enterprise AI transformation initiatives.
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.
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Relocation Assistance Provided: No