AI Data Engineer

Role Summary
T. Rowe Price is seeking an innovative Data AI Engineer to guide the development and deployment of scalable AI and data solutions that enable next generation data foundations and capabilities leveraging AI for data and data for AI. The successful candidate will be a hands-on contributor with deep technical skills, who can drive best practices and innovation in a collaborative, purpose-driven environment.

Responsibilities

• Technical design, development, and maintenance of advanced “AI for data” and “Data for AI” capabilities driving advanced data pipelines, analytics platforms, and AI/ML solutions.
• Collaborate with diverse set of team members varying from business stakeholders, data scientists, product owners, and other stakeholders to understand business requirements and translate them into robust technical solutions.
• Delivery of AI data foundations in the data supply chain process and data storage systems to ensure high-quality, reliable, and compliant data across the enterprise.
• Build and operationalize machine learning models, facilitating their integration into business workflows and production environments.
• Champion data governance, security, and regulatory compliance, ensuring alignment with T. Rowe Price’s standards and industry best practices.
• Evaluate and implement emerging technologies, frameworks, and tools to advance T. Rowe Price’s data and AI capabilities.
• Troubleshoot and optimize data solutions and platform performance, ensuring scalability and resilience.
• Document system architectures, processes, and best practices for technical and non-technical stakeholders.

Qualifications

Required:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)
• 2+ years of professional experience in data engineering, AI, or machine learning roles.
• Strong proficiency in Python (preferred), Java, or Scala.
• Experience with cloud data platforms (AWS, Azure, or GCP), preferably within a regulated industry.
• Hands-on expertise with big data technologies (Spark, Kafka) and modern data platforms (Snowflake)
• Proven track record deploying and maintaining machine learning models in production environments.
• Solid understanding of Agile methodologies and DevOps practices.
• Excellent communication, collaboration, and leadership skills.

Preferred:

• Experience in the asset management or financial services industry.
• Familiarity with MLOps, CI/CD pipelines, and model lifecycle management.
• Industry certifications in cloud or data engineering technologies.
• Knowledge of data privacy, compliance (e.g., GDPR), and industry regulations relevant to financial services.

FINRA Requirements

FINRA licenses are not required and will not be supported for this role.

Work Flexibility

This role is eligible for hybrid work, with up to three days per week from home.

WHAT TO EXPECT AFTER APPLYING
1. You will receive an email and text message to answer a few questions to verify your eligibility. If you apply for multiple jobs, you will receive separate invitations for each role and will need to respond to each. Estimated Time Commitment: 3-5 minutes

2. If you are eligible, you will be asked to record video responses to introductory questions with our vendor partner, HireVue. Take time to read all instructions carefully before responding. Some questions may require you to respond within a set time limit, or with limited retakes. You can use this opportunity to tell us more about your background and interest than we can learn from a resume alone. Estimated Time Commitment: 20-30 minutes