Senior AI Application Engineer, Enterprise AI

GE Healthcare GE Healthcare · Healthcare · Krakow, Lesser Poland, Poland · Digital Technology / IT

Senior AI Application Engineer role focused on developing and deploying GenAI and Agentic AI solutions within an enterprise healthcare setting. Responsibilities include full SDLC ownership, frontend/backend development, integrating ML/LLM models, and collaborating with MLOps/GenAIOps teams. Requires strong Python/TypeScript, experience with ML/GenAI frameworks, cloud platforms, RAG pipelines, and LLMOps.

What you'd actually do

  1. 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.
  2. Frontend: Developing modern, intuitive interfaces for AI applications (React/Next.js, TypeScript, or equivalent) with a strong focus on usability, accessibility, and AI explainability.
  3. 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.
  4. 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.
  5. Collaborating with Insight Leaders and business stakeholders on requirements gathering, project documentation, and development planning.

Skills

Required

  • Python
  • TypeScript/JavaScript
  • React/Next.js
  • FastAPI/Flask
  • TensorFlow
  • PyTorch
  • LangChain
  • AutoGen
  • Semantic Kernel
  • AWS Bedrock
  • Azure OpenAI
  • AWS
  • Azure
  • GCP
  • Docker
  • Kubernetes
  • CI/CD automation
  • RAG pipelines
  • vector databases
  • LLMOps / GenAIOps

Nice to have

  • Node.js
  • GraphQL
  • gRPC
  • LangGraph

What the JD emphasized

  • hands-on experience developing and deploying AI-powered or data-driven applications in enterprise environments
  • Proven track record implementing end-to-end AI systems
  • Strong experience in ML/GenAI frameworks
  • Deep experience with Docker, Kubernetes, and CI/CD automation for AI workloads
  • Demonstrated experience with RAG pipelines, vector databases
  • Solid understanding of LLMOps / GenAIOps integration patterns

Other signals

  • developing and delivering innovative GenAI and Agentic AI solutions
  • integrating machine learning and generative models into enterprise-grade software products
  • translating data science prototypes and GenAI models into scalable applications or services