{"schemaVersion":"jobsearcher.job.v1","id":"bcc0348f376eb0827458e070","url":"https://jobsearcher.com/jobs/bcc0348f376eb0827458e070","canonicalUrl":"https://jobsearcher.com/jobs/bcc0348f376eb0827458e070","title":"Artificial Intelligence Engineer","description":"Role Summary:We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.Technical Stack:LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.Frameworks: LangChain, LlamaIndex, and Hugging Face.Orchestration: LangGraph and Multi-Agent Systems (MAS).Development: Python, FastAPI, and Asynchronous Programming.RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.Deployment: Docker, Production API management, and LLM monitoring.Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.Key Responsibilities:Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.Design and deploy high-performance, scalable backend services using FastAPI and Async Python.Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.Containerize and deploy AI services via Docker to production environments.Required Qualifications:Hands-on experience building and deploying GenAI applications in a production setting.Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).Experience with vector search, embedding models, and advanced data retrieval patterns.Knowledge of model fine-tuning techniques and local LLM quantization/hosting.Familiarity with production-grade monitoring, API security, and CI/CD for ML.","company":"Photon","rawCompany":"photon","city":"Dallas","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-07-01T07:30:24.424Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Artificial Intelligence Engineer","description":"Role Summary:We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.Technical Stack:LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.Frameworks: LangChain, LlamaIndex, and Hugging Face.Orchestration: LangGraph and Multi-Agent Systems (MAS).Development: Python, FastAPI, and Asynchronous Programming.RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.Deployment: Docker, Production API management, and LLM monitoring.Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.Key Responsibilities:Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.Design and deploy high-performance, scalable backend services using FastAPI and Async Python.Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.Containerize and deploy AI services via Docker to production environments.Required Qualifications:Hands-on experience building and deploying GenAI applications in a production setting.Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).Experience with vector search, embedding models, and advanced data retrieval patterns.Knowledge of model fine-tuning techniques and local LLM quantization/hosting.Familiarity with production-grade monitoring, API security, and CI/CD for ML.","datePosted":"2026-07-01T07:30:24.424Z","dateModified":"2026-07-01T07:30:24.424Z","hiringOrganization":{"@type":"Organization","name":"Photon","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dallas","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"bcc0348f376eb0827458e070"},"url":"https://jobsearcher.com/jobs/bcc0348f376eb0827458e070"}}