Principal Data/AI Engineering

AT&T; New Jersey; On-site; USD 150,000 - 190,000; Full time

Job summary

AT&T is seeking a senior Data Platform Engineer to join our Data Science team in New Jersey. This full-time onsite role supports enterprise AI and data platforms, building scalable data pipelines and AI workflows in production environments.

You will collaborate with data scientists, AI engineers, and software developers to operationalize AI capabilities and drive business value through data-driven decisions.

Qualifications

• BS/BA in CS, Data Science, or related field preferred.
• 7+ years in data engineering, AI/ML operations, or related disciplines.
• Experience with enterprise-scale AI and data platforms in production.
• Knowledge of cloud, AI tooling, and MLOps frameworks.
• Proficiency with Python and SQL.
• Experience with structured, unstructured, and streaming data.
• Strong problem-solving and production support skills.

Responsibilities

• Support and optimize AI and data platforms for scalable operations.
• Design and maintain data pipelines and AI workflows.
• Monitor platform health and ensure data quality in production.
• Collaborate with cross-functional teams to operationalize AI capabilities.
• Work with data scientists, AI engineers, and developers to deliver value.

Skills

Python

SQL

MLOps

Data pipelines

Cloud computing

Education

Bachelor's degree in Computer Science or related field

Tools

Azure

AWS

Google Cloud

MLflow

Job description

This is a full-time onsite role based in the location(s) posted and is performed from the designated work location five days per week. No relocation is offered.

"AT&T will not hire any applicants for this position who require employer sponsorship now or in the future."

Join AT&T and reimagine the communications and technologies that connect the world. The Data Science team at AT&T leads transformation through data-driven decisions that enhance the customer experience and businesses worldwide. Leveraging one of the largest data sets available, we develop meaningful insights that drive continuous innovation. We utilize the latest technology to develop generative AI solutions with real business impacts. When you step into a career with AT&T, you won’t just imagine the future-you’ll create it.

Key Roles and Responsibilities

• Responsible for supporting and optimizing enterprise AI and data platforms to enable reliable, scalable, and secure AI-driven business operations.
• Designs, implements, and maintains data pipelines, platform automation, AI workflows, and operational processes that support machine learning and generative AI solutions.
• Monitors platform health, ensures data quality, manages production support activities, and drives continuous improvement of AI and data services.
• Collaborates with cross-functional teams to operationalize AI capabilities, enhance platform performance, and deliver business value through data-driven innovation.
• Collaborate with data scientists, AI engineers, software developers, and business stakeholders to operationalize AI solutions.

Qualifications

• Bachelor's degree (BS/BA) in Computer Science, Data Science, Information Systems or a related technical field preferred.
• 7+ years of experience in data engineering, AI/ML operations, platform engineering, analytics engineering, or related technology disciplines.
• Experience supporting enterprise-scale AI, machine learning, and data platforms in a production environment.
• Relevant cloud, AI, data engineering, or platform certifications may be required based on business needs.
• Strong knowledge of AI platforms, AI tooling, and MLOps frameworks.
• Experience designing, building, and maintaining scalable data pipelines, data integration solutions, and AI workflows.
• Proficiency with cloud-based data and AI ecosystems (e.g., Azure, AWS, Google Cloud).
• Experience supporting model deployment, monitoring, performance optimization, and
• Proficiency in Python, SQL, and modern data engineering technologies.
• Experience working with structured, unstructured, and streaming data environments.
• Ability to collaborate with data scientists, AI engineers, software developers, and business stakeholders to operationalize AI solutions.
• Strong problem-solving, troubleshooting, and production support experience with a focus on operational excellence and continuous improvement.