Principal ML Ops Engineer

Overview

The Enterprise Data & Analytics and Enterprise Data Platforms team is seeking a Principal ML Ops Engineer who will lead the design and operationalization of ML systems on AI/ML platforms such as AWS Sage Maker and H2O.ai. This role focuses on building scalable ML systems rather than individual models and includes leadership responsibilities for mentoring and grooming talent in global capability centers (GCC) with potential onshore leadership opportunities.

The position also requires hands‑on experience with GenAI, including building intelligent agents and exposure to Agentic AI concepts.

Candidates must be based in or willing to commute to one of the following hub locations with a hybrid schedule of four days onsite and one day remote per week with flexibility:

• Charlotte, NC (First choice)
• Westwood, MA – 200 Station Drive
• Iselin, NJ – 101 Wood Avenue South
• Boston, MA – 28 State Street
• Johnston, RI – One Citizens Bank Way

This is a strategic role at the intersection of ML Ops and Generative AI innovation. You will shape the future of enterprise AI by building robust ML systems that power advanced analytics and intelligent automation across the organization. Your leadership will influence engineering standards, accelerate AI adoption, and mentor the next generation of ML engineers. If you are passionate about operationalizing AI at scale and driving GenAI initiatives, this is an opportunity to make a significant impact.

• Lead and mentor engineering teams, including GCC talent development and potential onshore leadership
• Architect, design, and build ML engineering systems on the CFG ML Platform to accelerate ML pipeline delivery
• Develop and enhance platform capabilities and frameworks to standardize and automate ML pipeline deployment
• Implement capabilities such as feature stores, feature tracking, feature serving (real‑time and batch), 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 for ML pipeline development and deployment
• Collaborate with business teams, data science teams, enterprise architects, and security to uphold ML engineering standards
• Develop CI/CD pipelines for continuous integration and delivery of ML models
• Identify and automate ML pipeline and model deployment patterns to streamline workflows
• Troubleshoot and resolve issues related to ML system performance and deployment
• Contribute to GenAI initiatives, including building intelligent agents and integrating them into ML Ops workflows
• Demonstrate exposure to Agentic AI concepts and proof‑of‑concepts (POCs)

• 7+ years of experience with Python for scripting ML workflows
• 5+ years of experience deploying ML pipelines and systems using AWS Sage Maker
• 3+ years of experience developing APIs with Flask, Django, or FastAPI
• 2+ years of experience with ML frameworks and tools such as scikit‑learn, PyTorch, XGBoost, Light
  
  GBM, 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 and Kafka
• Preferred experience with H2O.ai
• Preferred experience with containerization using Docker and orchestration using Kubernetes
• Required exposure to GenAI and Agentic AI concepts, including building or contributing to POCs

Bachelor’s Degree or equivalent combination of education, training, and experience required

Hours per Week: 40

Schedule:

Monday–Friday

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.…