Job Description Summary

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Job Description

Role Overview

GE HealthCare is accelerating its transformation through a series of strategic AI Big Bets in Commercial excellence Logistics optimization Inventory management and Manufacturing innovation. The Enterprise AI team part of the Chief Data and Analytics Office is at the forefront of delivering robust enterprise-grade AI and ML solutions that drive measurable business impact at scale.

As the Staff AI Application Engineer you will be at the forefront of developing and delivering innovative GenAI and Agentic AI solutions that generate actionable business insights and transform key areas within GE HealthCare including Finance Commercial Supply Chain Quality Operational Excellence and Lean and Manufacturing. We are seeking a highly skilled and motivated AI Application Engineer to join our dynamic team. You will play a pivotal role in shaping and executing our AI strategy. Youll collaborate across a unified cross-functional delivery organizationpartnering with experts in data engineering ML engineering analytics and GenAI developmentto solve complex business challenges and deliver scalable solutions.

Core Responsibilities

• Design and develop AI-powered applications integrating machine learning and generative models into enterprise-grade software products and internal tools. Own the full software development lifecycle (SDLC) including unit integration and end-to-end testing.
• Frontend: Develop modern intuitive interfaces for AI applications (React/ TypeScript or equivalent) with a strong focus on usability accessibility and AI explainability.
• Backend: Implement scalable and secure back-end services (FastAPI Flask or ) to expose AI capabilities (LLMs RAG pipelines AI agents) through standardized APIs.
• Translate data science prototypes and GenAI models (LLMs diffusion models transformers) into scalable applications or services with intuitive user interfaces and reliable back-end infrastructure.
• Collaborate with insight leaders and business stakeholders on requirements gathering project documentation and development planning.
• Partners with MLOps and GenAIOps teams to deploy monitor and continuously improve AI applications within standardized CI/CD pipelines.
• Design and implement integrations using REST GraphQL and gRPC; work with cloud-based AI APIs (Azure AWS GCP) and enterprise data sources.
• Integrate cloud-native AI services (AWS Bedrock Azure OpenAI) and open-source frameworks (LangChain LangGraph) into enterprise environments.
• Monitor application performance and user adoption iterating on models and workflows to enhance usability and business impact.
• Optimize application performance infrastructure efficiency and LLM utilization.
• Document architectures APIs and deployment processes to ensure transparency reusability and maintainability.

Experience Requirements

• Education: Masters or PhD degree (or equivalent experience) in Computer Science Software Engineering Artificial Intelligence or related STEM field.
• Experience: 35 years of hands-on experience developing and deploying AI-powered or data-driven applications in enterprise environments.
• Advanced proficiency in Python plus strong working knowledge of TypeScript/JavaScript and at least one modern web framework (React FastAPI Flask).
• Proven track record implementing end-to-end AI systems integrating ML/LLM models into scalable microservices or enterprise applications.
• Strong experience in ML/GenAI frameworks (TensorFlow PyTorch LangChain AutoGen Semantic Kernel) and cloud-native AI platforms (AWS Bedrock Azure OpenAI).
• Working knowledge of cloud environments (AWS Azure or GCP) and containerization tools (Docker).
• Deep experience with Docker Kubernetes and CI/CD automation for AI workloads.
• Demonstrated experience with RAG pipelines vector databases and document retrieval frameworks.
• Solid understanding of LLMOps / GenAIOps integration patterns model evaluation and prompt optimization workflows.
• Strong collaboration skills and the ability to communicate effectively within cross-functional teams.
• Ability to mentor junior engineers perform code reviews and contribute to architectural decisions.
• Strong problem-solving debugging and analytical skills with clear and persuasive communication to technical and business audiences.