Senior Staff GenAI/ML Ops Engineer
GE HealthCare
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $136,000 - $204,000 per year
Job Level
Senior
Tech Stack
AirflowAWSAzureCloudDockerFlaskHadoopJenkinsKerasKubernetesPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
- 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.
Requirements
- Bachelors degree in Computer Science, Data Science, Engineering, or a related discipline with a strong focus on Machine Learning, Deep Learning, or AI Operations.
- 3–5 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).
- Familiarity with containerization, CI/CD, and DevOps practices (Docker, Kubernetes, GitHub Actions, Jenkins).
- Demonstrated expertise 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 understanding of 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.
Benefits
- medical
- dental
- vision
- paid time off
- 401(k) plan with employee and company contribution opportunities
- life insurance
- disability insurance
- accident insurance
- tuition reimbursement
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Machine LearningDeep LearningAI OperationsAPI developmentMLOpsGenAIOpsdata preprocessingfeature engineeringmodel evaluationprompt engineering
Soft skills
communicationcollaborationstakeholder managementinfluencecuriositycontinuous learning
Certifications
Bachelor's degree in Computer ScienceBachelor's degree in Data ScienceBachelor's degree in Engineering