Principal Architect GenAI and ML Ops

Job Description SummaryRole Overview
GE HealthCare’s 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 HealthCare’s 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.

Job Description

• PhD or Master’s 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.

Additional Information