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Full-Stack Generative AI Developer

Job in Pleasanton, Alameda County, California, 94566, USA
Listing for: Diverse Lynx
Full Time position
Listed on 2026-07-24
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Backend Developer, Full Stack Developer, Machine Learning/ ML Engineer
Job Description & How to Apply Below

Full-Stack Generative AI Developer

Seeking talented Full-Stack Generative AI Developer who will design, implement, and maintain cutting-edge AI-powered applications and services. In this role, developer will bridge the gap between advanced generative AI models and user-facing applications, creating intuitive interfaces for complex AI capabilities while ensuring robust, scalable, and efficient backend systems.

Key Responsibilities

  • AI Development
  • Design and implement generative AI features leveraging models like GPT-5, Llama, or other foundational models
  • Fine-tune and optimize AI models for specific use cases and domains (optional, good to have)
  • Create prompt engineering frameworks and templates to ensure consistent AI behavior
  • Develop retrieval-augmented generation (RAG) systems that combine vector databases with LLMs
  • Working knowledge of LLM evaluation and observability platforms
  • Frontend Development (Basic Exposure)
  • Build responsive, accessible web interfaces for AI-powered applications using modern frameworks (React, Vue, Angular, MUI)
  • Design intuitive UIs that make complex AI capabilities understandable and usable
  • Implement real-time features for AI interactions (streaming responses, progressive rendering)
  • Develop visualization components for AI outputs and confidence metrics
  • Backend Development
  • Create robust APIs on python and similar framework and services to interface with AI models and external services
  • Design scalable architectures that can handle high-throughput AI inference
  • Implement efficient data processing pipelines for AI training and inference
  • Integrate with vector databases, document stores, and other AI-specific infrastructure
  • Dev Ops & MLOps
  • Set up CI/CD pipelines for AI application deployment
  • Implement monitoring, logging, and alerting for AI systems
  • Optimize infrastructure for cost-effective AI deployment
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