Job Description Summary

Role 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