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# Generative AI Engineer

- **Company:** Aivra Health Llc
- **Location:** San Francisco, CA, United States
- **Experience:** Starter
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Continuous Integration, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Tensorflow, Standard Sql, Search Technologies, Software Engineering, Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, Build Management, AI Platforms, Kubernetes, Information Technology, Low Latency, HuggingFace, Machine Learning Operations, Restful APIs, Docker
- **Published:** September 26, 2026
- **Apply:** https://www.dice.com/job-detail/77045a98-72f8-4e4c-91e9-5c17b6d3fa8b

## About the Role

* Python
 * Generative AI / LLMs
 * OpenAI / Gemini / Claude APIs
 * RAG
 * Prompt Engineering
 * LangChain / LlamaIndex
 * Hugging Face
 * Vector Databases
 * REST APIs
 * Git / GitHub
 * SQL
 * Machine Learning fundamentals

Preferred Skills

 * PyTorch / TensorFlow
 * AWS / Azure / Google Cloud Platform
 * Docker / Kubernetes
 * Fine-tuning / LoRA / PEFT
 * NLP
 * AI Agents
 * MCP
 * PostgreSQL / MongoDB
 * Pinecone / Weaviate / FAISS
 * MLOps / CI/CD, Master's degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related technical field., * s and candidates with 1-3 years of experience.
 * Currently living in the USA.
 * Relevant AI/ML, GenAI, software development, internship, or project experience.
 * Valid US work authorization, where required (F1-OPT/STEM opt/H1-B).
 * Open to Work / actively seeking opportunities.

## Description

We are looking for a Generative AI Engineer to build and deploy AI-powered applications using modern LLMs, generative AI frameworks, and machine learning technologies., * Develop and deploy LLM and Generative AI applications.

 * Build RAG (Retrieval-Augmented Generation) pipelines.
 * Work with embeddings, vector databases, and semantic search.
 * Integrate LLM APIs into production applications.
 * Fine-tune and evaluate AI/ML models where appropriate.
 * Develop AI agents and intelligent automation workflows.
 * Optimize model performance, latency, and cost.
 * Collaborate with software engineers and product teams.
 * Build APIs and production-ready AI services.