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 Engineer, 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.
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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.

Experience & Qualifications

• 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.
• 1-3 years of hands-on experience in developing, deploying, and maintaining ML/AI development pipelines and applications in enterprise environments.
• Knowledge of API development and orchestration frameworks (FastAPI, Flask, Airflow).
• Demonstrated expertise in MLOps / GenAIOps tools and frameworks (e.g., MLflow, SageMaker, Bedrock , LangSmith, LangGraph).
• Experience in Python, cloud platforms (AWS, Azure), and open-source data science tools (Jupyter, SQL, Hadoop, Spark, TensorFlow, Keras, PyTorch, Scikit-learn).
• Understanding of containerization, CI/CD, and DevOps practices (Docker, Kubernetes, GitHub Actions, Jenkins).
• Experience with data preprocessing, feature engineering, and model evaluation in real-world, large-scale environments.
• Experience with LLMs and generative AI models, including transformers, diffusion models, self-supervised learning, and prompt engineering.
• 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.
• Problem-solving, debugging, and analytical skills, with clear and persuasive communication to technical audiences.

Skills Required