The Data/AI Engineer is an individual contributor role focused on advancing AT&T’s enterprise-scale AI solutions by developing, deploying, and maintaining robust machine learning operations pipelines and data engineering solutions. This role requires a strong blend of AI, data engineering, and MLOps expertise, ideally with exposure to technology, fraud management, and financial or risk domains. The ideal candidate thrives in agile product development environments and has a proven track record delivering scalable, production-ready AI products.
Key Responsibilities
Design, build, and optimize scalable data pipelines for ingestion, processing, and integration from diverse data sources.
Develop and deploy AI and machine learning models into production environments, ensuring reliability and scalability.
Utilize cloud platforms (AWS, Azure, GCP) and modern data technologies (e.g., Snowflake, Databricks, Kafka) to manage large-scale data workflows.
Collaborate closely with data scientists and analysts to translate analytical models into operational AI solutions.
Implement data quality checks, monitoring, and alerting to ensure data integrity and model performance.
Support continuous integration and continuous deployment (CI/CD) processes for AI and data workflows.
Apply best practices for data security, privacy, and compliance within all engineering solutions.
Troubleshoot and resolve data and AI system issues in production environments.
Document architecture, processes, and technical specifications to ensure maintainability and knowledge sharing.
Stay up-to-date with emerging technologies and industry trends in data engineering and AI.
Experience working in agile/scrum product development teams.
Strong analytical and problem-solving skills with a client-focused mindset.
Excellent communication and presentation skills for conveying complex analytics and AI concepts to diverse audiences.
Qualifications
Required:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
Proven experience in data engineering and AI/ML model deployment in enterprise environments.
Proficiency in programming languages such as Python, SQL, and familiarity with Spark or similar distributed computing frameworks.
Experience with cloud data platforms and services (AWS, Azure, GCP).
Strong understanding of data pipeline architecture, ETL/ELT processes, and data warehousing concepts.
Familiarity with containerization (Docker) and orchestration tools (Kubernetes) is a plus.
Excellent problem-solving skills and ability to work collaboratively in cross-functional teams.
Preferred:
Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
Knowledge of real-time data streaming technologies (Kafka, Kinesis).
Understanding of data governance, data privacy regulations, and responsible AI principles.
Experience with CI/CD pipelines and automation tools for ML workflows.
Strong communication skills for technical and non-technical audiences.
Familiarity with Generative AI, Large Language Model (LLM) workflows, and Graph-based Retrieval-Augmented Generation (RAG) techniques.
Background in telecommunications, fraud management, financial services, or risk analytics.
Knowledge of responsible AI practices, data governance, and bias mitigation strategies.
Willingness to work flexible shifts as required to support business operations.
It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made.
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