Principal Architect GenAI and ML Ops

Role Overview
GE HealthCares Chief Data and Analytics Office (CDAO) delivers innovative data insights and AI solutions across the organization. Our Enterprise AI team drives a diverse portfolio of Machine Learning (ML) Artificial Intelligence (AI) and Generative AI (GenAI) initiatives by combining agile execution with industry-leading methods and tools.
As a GenAI/ML Ops Director you will be at the forefront of operationalizing advanced Machine Learning and Generative AI solutions. You will design deliver and maintain robust development and deployment pipelines for high-impact AI applications across key business domains within GE HealthCare including Finance Commercial Supply Chain Quality Operational Excellence Lean and Manufacturing.
We are seeking a highly skilled and motivated engineer experienced in ML and GenAI operations software development and AI architecture to join our dynamic and growing team.
Core Responsibilities
Develop and operationalize ML and GenAI pipelines to enable scalable reliable and secure deployment of AI models across GE HealthCares enterprise landscape.
Automate model lifecycle management including model versioning continuous integration (CI/CD) testing deployment observability and monitoring and governance in alignment with enterprise standards.
Partner with IT and cloud teams to optimize infrastructure for AI workloads across hybrid and multi-cloud environments (AWS Azure)
Collaborate with cross-functional teams including data scientists software engineers architects and domain experts to ensure smooth end-to-end delivery of AI solutions.
Integrate Generative AI capabilities (e.g. LLMs multimodal models) into business workflows enhancing automation productivity and decision intelligence.
Conduct research and proof-of-concepts to evaluate emerging tools frameworks and architectures for GenAI and ML Ops (e.g. LangChain MLflow Kubeflow MS Copilot OpenAi Agent Builder)
Mentor and guide data science and engineering teams on best practices in productionizing AI models and managing their lifecycle.
Promote a culture of innovation collaboration and continuous improvement within the Enterprise AI team.

• PhD or Masters degree in Computer Science Data Science Engineering or a related discipline with a strong focus on Machine Learning Deep Learning or AI Operations.

• 10 years of hands-on experience in developing deploying and maintaining ML/AI development pipelines and applications in enterprise environments.

• Excellent knowledge of API development and orchestration frameworks (FastAPI Flask Airflow).

• Mastery in MLOps / GenAIOps tools and frameworks (e.g. MLflow SageMaker Bedrock LangSmith LangGraph).

• Proficiency in Python cloud platforms (AWS Azure) and open-source data science tools (Jupyter SQL Hadoop Spark TensorFlow Keras PyTorch Scikit-learn).

• Strong working knowledge of containerization CI/CD and DevOps practices (Docker Kubernetes GitHub Actions Jenkins).

• Proven experience in data preprocessing feature engineering and model evaluation in real-world large-scale environments.

• Strong experience with LLMs and generative AI models including transformers diffusion models self-supervised learning and prompt engineering.

• Proficiency in cloud architecture design (AWS Azure or GCP) including cost optimization scaling and secure data governance.

• Proven ability to translate research and prototypes into scalable enterprise-grade solutions.

• Excellent communication collaboration and stakeholder management skills with the ability to influence both technical and executive audiences.

• Curiosity and drive for continuous learning staying current with advances in GenAI MLOps and AI infrastructure technologies.

• Experience with vector databases (e.g. Pinecone FAISS Milvus) and retrieval-augmented generation (RAG) pipelines.

• Experience with LLM prompt engineering and LangChain architecture

• Strong understanding of multi-agent or distributed AI ecosystems enabling consistent model-to-model communication (MCP A2A) and orchestration.