Sr Data Scientist - Gen AI ML - Irving

Photon•United States,

4d•$53,000 - $188,000•Onsite

About The Position

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.

Requirements

7+ years of experience ; 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.

Nice To Haves

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.

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.