What are the responsibilities and job description for the Sr. GenAI Developer position at Ravin IT Solutions?
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 GCP 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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