Sr. GenAI Developer

Overview

On Site
Depends on Experience
Accepts corp to corp applications
Contract - Independent
Contract - W2

Skills

Sr. GenAI Developer
Python

Job Details

Hello,

We have an immediate Openings with Our Direct Client for a Long-term contract position

Job Title: Sr. GenAI Developer

Location: Irving TX, Columbus Ohio / Atlanta GA

GenAI (6+ Months)

  • Expertise in providing run support for applications and products.
  • Experience with GenAI technologies like CoPilot and Codeium is preferred.
  • Be able to quickly fix the problems for end users
  • Knowledge of the data and underlying architecture to facilitate the application of GenAI at Verizon
  • Be able to work on unique projects to create and incorporate GenAI technology to improve developer capabilities and experience

Technical Expertise

  • Strong understanding of Generative AI tools: Hands-on experience with GitHub Copilot, self-hosted generative AI platforms, or similar tools, including installation, configuration, and maintenance.
  • Programming expertise: Proficiency in programming languages such as Python, JavaScript, or other languages commonly used with AI/ML tools.
  • Containerization and orchestration: Experience with Docker and Google Kubernetes Engine for deploying and managing self-hosted generative AI tools.
  • Cloud platforms: Familiarity with AWS and Google Cloud Platform to support hosting and scaling AI products in cloud environments.

Operational Expertise

  • Monitoring and troubleshooting: Proven experience in monitoring AI systems for performance, identifying bottlenecks, and troubleshooting issues to maintain high availability.
  • Optimization: Ability to fine-tune generative AI models or systems for improved performance, latency reduction, and user experience.

Generative AI-Specific Knowledge

  • Prompt engineering: Understanding of prompt design, token optimization, and use case adaptation for generative AI tools.
  • Model usage insights: Ability to analyze usage metrics (e.g., token consumption, accuracy, and failure rates) and derive insights for improving user adoption and efficiency.
  • Self-hosted model operations: Experience managing fine-tuning, updating, or upgrading models hosted on proprietary infrastructure.
  • Knowledge of ML frameworks: Familiarity with frameworks such as TensorFlow, PyTorch, or Hugging Face for working with custom AI models if required.

Preferred Qualifications

  • Experience with large language models (LLMs) like OpenAI GPT, Anthropic, or similar tools.
  • Exposure to enterprise-scale generative AI deployments.
  • Certifications in AI/ML or cloud platforms (e.g., AWS Certified Machine Learning, Google Cloud AI Engineer).
  • Familiarity with DevOps principles to improve the deployment and monitoring lifecycle of generative AI tools.

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