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# Generative AI Engineer

- **Company:** Robotics Technologies LLC
- **Location:** Salt Lake City, UT, United States
- **Experience:** Experienced
- **Salary:** $176,800
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Cloud Computing, Cloud Engineering, Databases, Memory Management, Information Retrieval, Python (Programming Language), Machine Learning, Performance Tuning, OpenAI, Search Technologies, Software Deployment, Software Engineering, Systems Integration, Pinecone, Google Cloud, Enterprise Software Applications, Chatbots, LangChain, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Google Vertex AI, Model Validation, Llamaindex, Generative AI, Backend, Agentic-AI, Fastapi, Containerization, AI Platforms, Kubernetes, Weaviate, FAISS, Evaluation of Large Language Models, Invoking Functions, Restful APIs, Model Context Protocol, Docker, ChromaDB
- **Published:** November 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f91cf62478fc8450

## About the Role

* 4-8 years of experience in software development, AI engineering, or related fields, with hands-on Generative AI experience.
 * Strong proficiency in Python and experience building RESTful APIs.
 * Practical experience with LLMs, RAG architecture, prompt engineering, and embeddings.
 * Hands-on knowledge of LangChain, LangGraph, or LlamaIndex.
 * Experience with vector databases and semantic search technologies.
 * Familiarity with AWS, Azure, or Google Cloud AI services.
 * Understanding of NLP, machine learning fundamentals, model evaluation, and AI security.
 * Strong analytical, problem-solving, and communication skills.

## Description

The Generative AI Engineer will be responsible for developing intelligent, AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI technologies. This position involves building enterprise AI solutions, designing conversational interfaces, integrating foundation models, and developing intelligent automation workflows. The ideal candidate should have strong Python programming experience and practical knowledge of modern AI frameworks, prompt engineering, vector databases, and cloud-based AI services., * Design, develop, and deploy Generative AI applications using OpenAI, Azure OpenAI, Anthropic, and other LLM platforms.

 * Build intelligent chatbots, virtual assistants, enterprise copilots, and conversational AI solutions.
 * Develop prompt engineering strategies, including zero-shot, few-shot, and chain-of-thought prompting.
 * Integrate LLM capabilities into enterprise applications through APIs and backend services.
 * Optimize model responses for accuracy, relevance, latency, and cost efficiency.

RAG Pipelines & Knowledge Retrieval:

 * Architect and implement Retrieval-Augmented Generation (RAG) pipelines for enterprise data.
 * Develop document ingestion, text chunking, embedding generation, and semantic search workflows.
 * Work with vector databases such as Pinecone, ChromaDB, FAISS, and Weaviate.
 * Implement hybrid search, metadata filtering, reranking, and context retrieval techniques.
 * Connect LLM applications with internal knowledge bases, databases, and external data sources.

Agentic AI & Intelligent Automation:

 * Develop AI agents and multi-agent workflows using LangChain, LangGraph, and LlamaIndex.
 * Implement tool calling, function calling, memory management, and autonomous task execution.
 * Build workflow orchestration solutions using agent-based architectures.
 * Integrate AI agents with REST APIs and enterprise applications.
 * Explore Model Context Protocol (MCP) for connecting AI systems with external tools and services.

Cloud Deployment & AI Operations:

 * Develop backend services using Python, FastAPI, and Flask.
 * Deploy AI applications using AWS Bedrock, Azure AI Foundry, or Google Vertex AI.
 * Containerize applications using Docker and support Kubernetes-based deployments.
 * Implement LLM evaluation, monitoring, guardrails, and responsible AI practices.
 * Maintain CI/CD pipelines and support production deployment and performance optimization.