AI Engineer / Data Scientist with Python , ML/AI , LLM - Dallas TX - Onsite work - In person Interview on Tuesday

AI Engineer / Data Scientist with Python , ML/AI , LLM - Dallas TX - Onsite work - In person Interview on Tuesday

Rate: 50-55 $ phr on W2 or 60-65 $ phr on Corp- Corp all Inc

About the Role

We're looking for a multi-disciplinary AI Engineer to design, implement, and deploy LLM-driven agents with strong backend and front-end integration. You'll be leading efforts across LLM agent design, prompting, fine-tuning, and MLOps, while building real-world, production-grade applications with modern web technologies.

The ideal candidate combines a strong foundation in Python and AI with practical experience in agent frameworks like LangGraph, PydanticAI, and Google ADK, as well as FastAPI and front-end development.

Key Responsibilities

• LLM Agents & Prompt Engineering Architect and implement LLM agents using frameworks like LangGraph, PydanticAI, and Google ADK.
• Build composable, tool-augmented reasoning chains (e.g., RAG, COT, ReAct, planner-executor).
• Integrate vector databases (e.g., FAISS, Pinecone, pgvector) and knowledge graphs (Neo4j) to support retrieval-augmented generation (RAG) and long-term chatbot memory.
• Design and maintain high-quality prompt strategies for robustness and reliability. FastAPI, Model Context Protocol (MCP) & Backend
• Develop and maintain scalable APls using FastAPI, supporting synchronous and asynchronous agent execution.
• Integrate Model Context Protocol (MCP) to enable secure and structured access to external data and tools within agent work flows.
• Implement state tracking, context-aware input dispatch, and modular plugin integration within the control plane.Evaluation, Testing & Observability
• Build unit and behavioral tests for agents, tools, and workflows.
• Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
• Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.Model Fine-Tuning & MLOps
• Fine-tune models using LoRA, QLoRA, or full fine-tuning pipelines.
• Integrate, deploy, and monitor models in production using Cloud Providers.
• Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.Model Fine-Tuning & MLOps
• Fine-tune models using LoRA, QLoRA, or full fine-tuning pipelines.
• Integrate, deploy, and monitor models in production using cloud providers.
• Set up agent logging, observability dashboards, and recovery workflows.Front-end & User Experience
• Collaborate with front-end developers or build user-facing components using React, Typescript, or Next.js.
• Ensure seamless user and agent interaction via UI and API bridges.

Required Skills & ExperienceCore Skills:

• 3+ years experience with Python in ML/Al systems and PyTorch or Tensorflow
• 1+ years experience with LLM agent development, prompt engineering, and frameworks like LangGraph, PydanticAI, and Google ADK.
• Experience with fine-tuning LLMs.
• Familiarity using vector stores like ChromaDB, Weaviate, or pgvector.
• Production experience with FastAPI, Docker, and MLOps
• Expert in Agentic Coding IDEs (Windsurf, Cursor or Claude Code)
• Bachelor's or Master's degree in computer science Front- end & UX:
• Familiar with React, Typescript, Next.js, or similar frameworks
• Understanding of front-end and back-end integration for Al tools
• Ability to build basic dashboards or agent interfacesBonus Experience
• Open-source contributions to LLM/agent tooling
• Knowledge of async programming, websockets, and streaming APls