**Job Description:** Role Overview As a Specialist Data/AI Engineer – QA at AT&T, you will be responsible for ensuring the quality, reliability, and performance of data pipelines, AI models, and analytics solutions. You will design and execute comprehensive testing strategies for data and AI systems, including validation of data integrity, model accuracy, and system scalability. Your role is critical to delivering robust, production-ready AI and data solutions that meet AT&T’s high standards. Key Responsibilities + Develop and implement QA frameworks, test plans, and automated testing scripts for data pipelines and AI/ML models. + Validate data quality, consistency, and accuracy across ingestion, transformation, and storage processes. + Test AI/ML model performance including accuracy, bias, robustness, and drift detection. + Utilize cloud platforms (AWS, Azure, GCP) and modern data technologies (e.g., Snowflake, Databricks, Kafka) to manage large-scale data workflows. + Collaborate with data engineers, data scientists, and product teams to identify test requirements and ensure comprehensive coverage. + Perform regression, integration, system, and performance testing on data and AI workflows. + Automate testing processes using appropriate tools and frameworks to enable continuous testing in CI/CD pipelines. + Monitor production systems to detect issues proactively and support root cause analysis for defects or anomalies. + Document test results, defects, and quality metrics, communicating findings to technical and non-technical stakeholders. + Advocate for quality best practices and contribute to improving testing methodologies across the CDO. + Stay current with industry trends and emerging tools in data engineering, AI, and QA automation. Qualifications Required: + Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. + Experience in quality assurance or testing roles focused on data engineering, AI, or machine learning systems. + Proficiency in programming and scripting languages such as Python, SQL, and experience with test automation frameworks. + Strong understanding of data pipelines, ETL/ELT processes, and data validation techniques. + Familiarity with machine learning concepts and model evaluation metrics. + Experience with cloud platforms (AWS, Azure, GCP) and data platforms (Snowflake, Databricks) is preferred. + Knowledge of CI/CD tools and integration of automated testing within deployment pipelines. + Excellent analytical, problem-solving, and communication skills. Preferred: + Experience with AI/ML model testing frameworks and bias/fairness testing. + Familiarity with containerization (Docker) and orchestration (Kubernetes) environments. + Understanding of data governance, compliance, and responsible AI principles. + Experience with real-time data streaming and testing associated workflows. \#DataEngineering **Weekly Hours:** 40 **Time Type:** Regular **Location:** IND:AP:Hyderabad / Argus Bldg 4f & 5f, Sattva, Knowledge City- Adm: Argus Building, Sattva, Knowledge City 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. AT&T will consider for employment qualified applicants in a manner consistent with the requirements of federal, state and local laws We expect employees to be honest, trustworthy, and operate with integrity. Discrimination and all unlawful harassment (including sexual harassment) in employment is not tolerated. We encourage success based on our individual merits and abilities without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, disability, marital status, citizenship status, military status, protected veteran status or employment status