Principal Ai/ml Engineer

Verizon Verizon · Telecom · Temple Terrace, FL

Principal AI/ML Engineer at Verizon responsible for implementing, tuning, and scaling AI/ML applications, leading cloud solution planning and deployment, and providing application design guidance. The role involves building ML pipelines on Hadoop, investigating and implementing software solutions, and supporting 5G/Intelligent Edge Network initiatives. Requires experience with LLMs, VectorDB, GraphDB, finetuning LLMs, and evaluating GenAI use cases.

What you'd actually do

  1. Responsible for AI/ML applications implementation, performance tuning, and scaling (20%)
  2. Lead the cloud solutions planning, configuration, deployment, and operation (20%)
  3. Provide application design guidance to modernize existing applications or write new AI applications (20%)
  4. Lead reporting and analytical systems to solve Big Data problems that drive customer satisfaction throughout our business (10%)
  5. Build and handle machine learning pipelines on a Hadoop cluster to support any kind of model deployment on streaming or batch data using tools like PySpark and Jupyter Notebook (10%)

Skills

Required

  • Python
  • Unix Shell
  • Perl
  • JavaScript
  • GCP
  • Big Data
  • PySpark
  • Jupyter
  • Kubernetes Engine
  • CI/CD tools
  • Concourse
  • Jenkins
  • monitoring tools
  • App Dynamics
  • NewRelic
  • Grafana
  • large language models (LLMs)
  • VectorDB
  • GraphDB
  • finetuning LLMs
  • model performance evaluation
  • Retrieval
  • Generation
  • GenAI use cases

Nice to have

  • Hadoop cluster
  • streaming data
  • batch data

What the JD emphasized

  • large language models (LLMs)
  • VectorDB
  • GraphDB
  • finetuning LLMs
  • model performance evaluation
  • Retrieval
  • Generation
  • GenAI use cases

Other signals

  • AI/ML applications implementation, performance tuning, and scaling
  • Build and handle machine learning pipelines
  • working with large language models (LLMs), VectorDB, GraphDB, and finetuning LLMs, and model performance evaluation, including Retrieval, Generation, and other GenAI use cases