Technology

Sr. Gen AI Engineer

PhotonIrving, TX🇺🇸United StatesPosted Oct 5, 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to build cutting-edge Generative AI applications, working with a wide array of LLMs and advanced frameworks. You'll thrive here if you possess strong machine learning expertise combined with robust software engineering skills, eager to contribute to innovative AI solutions. Apply now to join a forward-thinking team and advance your career in this dynamic field.

Quick Overview

Seniority

Mid Senior

Work mode

Hybrid

Location

Irving, TX, United States

Posted

2 weeks ago

DockerFastAPIMachine LearningGenerative AIHugging FaceLLMPostgreSQLPyTorchPythonTensorFlow

Job Description

Job Title: Sr. Gen AI Engineer

Location: Irving, TX / Jacksonville, FL / New York, NY

Duration: Long Term

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.