Lead Data/ai Engineering

AT&T AT&T · Telecom · Dallas, TX

Lead Data/AI Engineer responsible for managing stakeholder relationships, translating business objectives into data manipulation requirements, and driving AI Technology platform strategy. This role involves optimizing AI/ML tooling, overseeing data science solutioning in areas like Computer Vision and OCR, ensuring accuracy of data science outputs, and integrating software development with data science artifacts. The position requires thought leadership, executive presentations, mentoring, and potentially contributing to patents and white papers. Technical skills include JVM, Spark, Python, PySpark, JavaScript, SQL, Databricks, AI-Knowledge Graph, AI-Computer Vision, and OCR.

What you'd actually do

  1. Manage stakeholder relationship, analyze and interpret business objectives, transform those needs into relevant data manipulation requirements, and anticipate risks, mitigate roadblocks, and execute outputs for leadership.
  2. Provide thought leadership to executives that drive AI Technology platform strategy to align and optimize AI/ML tooling to facilitate highly productive Data Science solutioning including AI-Computer Vision, Data & Visual Analytics (DVA), Optical Character Recognition (OCR), and AI-Knowledge Graph.
  3. Acquire buy-in that must be secured from key leadership stakeholders to sponsor key strategies.
  4. Provide thought leadership to executives that drive best-in-class software development that must be integrated with the data science artifacts, developed and manipulated to test by data scientists and testing iterations must be performed.
  5. Review data science outputs of entire solution and vet for accuracy.

Skills

Required

  • Bachelor degree in Computer Science, Math, Scientific Computing, or Computer Engineering
  • 3 years of experience in the job offered or related occupation
  • JVM-based function languages
  • HDFS-based computing frameworks including Spark
  • Python programming
  • JavaScript Data Visualization
  • PySpark
  • AI-Knowledge Graph
  • AI-Computer Vision
  • Image Processing
  • Optical Character Recognition (OCR) model packages
  • Big Data technologies
  • Databricks
  • SQL

Nice to have

  • Data & Visual Analytics (DVA)

What the JD emphasized

  • must be secured
  • must be integrated
  • must be performed
  • AI-Computer Vision
  • Optical Character Recognition (OCR)
  • AI-Knowledge Graph

Other signals

  • Thought leadership to executives that drive AI Technology platform strategy
  • align and optimize AI/ML tooling to facilitate highly productive Data Science solutioning
  • Review data science outputs of entire solution and vet for accuracy
  • Apply knowledge with JVM-based function languages
  • Utilize alternative HDFS-based computing frameworks including Spark
  • Utilize Python programming, JavaScript Data Visualization
  • Utilize PySpark, AI-Knowledge Graph, and AI-Computer Vision
  • Utilize Image Processing and Optical Character Recognition (OCR) model packages
  • Utilize Big Data technologies and multiple programming languages including Databricks, Python, PySpark, and SQL