Principal ML Ops Engineer

Principal ML Ops Engineer

The Enterprise Data & Analytics and Enterprise Data Platforms team is seeking a Principal ML Ops Engineer with expertise in operationalizing ML pipelines on AI/ML platforms such as AWS Sagemaker and H2O.ai.

Role Overview:

As a Principal ML Ops Engineer, you will lead the development of ML Ops processes and standards, utilizing code boilerplates and model inferencing frameworks to operationalize ML pipelines (including data/feature pipelines) on enterprise AI platforms. You will drive automation efforts to reduce time-to-market for ML use cases by identifying and optimizing data pipeline, model pipeline, and model consumption patterns. Collaboration with stakeholders across the organization will be key to integrating ML pipelines with business applications.

Responsibilities:

• Architect, design, and build ML engineering capabilities on the CFG ML Platform to accelerate ML pipeline build and delivery.
• Develop and enhance platform capabilities and frameworks to standardize and automate the deployment of ML models and pipelines.
• Implement capabilities such as feature stores, feature tracking, feature serving (real-time, batch), real-time features, model performance monitoring, model lineage tracking, model health, and model serving and consumption (real-time, batch, event-triggered, near real-time using Kafka).
• Define processes, research market trends, and implement best practices to develop and deploy ML pipelines and standards on the CFG ML Platform.
• Collaborate with stakeholders such as business teams, data science teams, enterprise architects, and security to uphold the highest standards of ML engineering practices and tools on the CFG platform.
• Develop CI/CD pipelines for continuous integration and delivery of ML models.
• Identify and automate ML pipeline and model deployment patterns to streamline ML workflows and processes.
• Troubleshoot and resolve issues related to ML model deployment and performance.

Key Requirements:

• 7+ years of experience with Python for scripting ML workflows.
• 5+ years of experience deploying ML pipelines and models using AWS Sagemaker.
• 3+ years of experience developing APIs with Flask, Django, FastAPI.
• 2+ years of experience with ML frameworks and tools such as scikit-learn, PyTorch, XGBoost, LightGBM, MLflow.
• Solid understanding of the ML lifecycle: model development, training, validation, deployment, and monitoring.
• Solid understanding of CI/CD pipelines for ML workflows using Bitbucket, Jenkins, Nexus.
• Experience with ETL processes for ML pipelines using Spark, Kafka.
• Preferred experience with H2O.ai.
• Preferred experience with containerization using Docker and orchestration using Kubernetes.

Education & Certifications:

• Bachelor's Degree or equivalent combination of education, training, and experience required, along with high technical competency to perform the needed responsibilities.

Hours & Work Schedule:

• Hours per Week: 40
• Work Schedule: Monday-Friday

Pay Transparency

The salary range for this position is $175,000 - $ 230,000 per year  plus an opportunity to earn an annual discretionary bonus. Actual pay is based on various factors including but not limited to the work location, and relevant skills and experience.  

We offer competitive pay, comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more. Note, Citizens’ paid time off policy exceeds the mandatory, paid sick or paid time-away policy of very local and state jurisdiction in the United States. For an overview of our benefits, visit https://jobs.citizensbank.com/benefits.

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Equal Employment Opportunity

Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or lo