GenAI and ML Ops Engineer

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 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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Job DescriptionCore 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.