Job Description Summary

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Job Description

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.

Experience & Qualifications

• 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.
• 13 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.