Application Support Engineer providing Level 2 support and monitoring for application stability. Collaborating with Engineering and analyzing logs within a modern SaaS company framework.
About the role
- Staff AI Application Engineer developing AI solutions at GE HealthCare. Collaborating across teams to deliver scalable AI-powered applications and insights.
Responsibilities
- Design and Develop: AI-powered applications, integrating machine learning and generative models into enterprise-grade software products and internal tools.
- Owning the full software development lifecycle (SDLC), including unit, integration, and end-to-end testing.
- Frontend: Developing modern, intuitive interfaces for AI applications (React/Next.js, TypeScript, or equivalent) with a strong focus on usability, accessibility, and AI explainability.
- Backend: Implementing scalable and secure back-end services (FastAPI, Flask, or Node.js) to expose AI capabilities (LLMs, RAG pipelines, AI agents) through standardized APIs.
- Translating data science prototypes and GenAI models (LLMs, diffusion models, transformers) into scalable applications or services with intuitive user interfaces and reliable back-end infrastructure.
- Collaborating with Insight Leaders and business stakeholders on requirements gathering, project documentation, and development planning.
- Partnering with MLOps and GenAIOps teams to deploy, monitor, and continuously improve AI applications within standardized CI/CD pipelines.
- Designing and implementing integrations using REST, GraphQL, and gRPC; work with cloud-based AI APIs (Azure, AWS, GCP) and enterprise data sources.
- Integrating cloud-native AI services (AWS Bedrock, Azure OpenAI) and open-source frameworks (LangChain, LangGraph) into enterprise environments.
- Monitoring application performance and user adoption, iterating on models and workflows to enhance usability and business impact.
- Optimizing application performance, infrastructure efficiency, and LLM utilization.
- Documenting architectures, APIs, and deployment processes to ensure transparency, reusability, and maintainability.
Requirements
- Master’s or PhD degree (or equivalent experience) in Computer Science, Software Engineering, Artificial Intelligence, or related STEM field.
- 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, Next.js, 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.
Benefits
- Relocation Assistance Provided
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