Backend Engineer - Python- Dallas, TX

We are looking for a Python Backend Engineer to join our team building Agentic AI products. You will be responsible for developing the robust, scalable infrastructure that allows autonomous agents to interact with the real world.

Your work will involve creating secure execution environments, managing complex tool-integration layers, and optimizing the retrieval-augmented generation (RAG) pipelines that give our agents their "intelligence." You will bridge the gap between raw model outputs and reliable, production-ready software actions.

Key Responsibilities

• Agent Logic & Tooling: Develop and maintain the backend "tools" (APIs, scrapers, database connectors) that AI agents use to perform tasks.
• Orchestration Implementation: Use frameworks like LangChain, LangGraph, or CrewAI to implement complex reasoning chains and multi-agent coordination.
• RAG Pipeline Engineering: Build and optimize data ingestion and retrieval systems using Vector Databases, ensuring the agent has the right context at the right time.
• Asynchronous Task Management: Manage long-running AI reasoning cycles using asynchronous Python (FastAPI/Asyncio) and task queues like Celery or Redis.
• API Architecture: Design and implement secure, high-performance REST or GraphQL APIs that serve as the interface between the agentic backend and the frontend.
• Safety & Guardrails: Implement backend-level validation and guardrails to ensure that autonomous agent actions remain within secure and ethical boundaries.

Technical Requirements

• Python Expertise: 8+ years of professional experience with Python, specifically with FastAPI, Pydantic, and Asyncio.
• AI Frameworks: Hands-on experience with LangChain or LlamaIndex.
• Database Management: Proficiency in PostgreSQL and experience with Vector Databases.
• Cloud & DevOps: Experience deploying containerized applications using Docker and Kubernetes on AWS, Azure, or GCP.
• Scalability: Understanding of distributed systems and how to handle the high latency and compute requirements of LLM-based applications.
• Version Control: Mastery of Git and CI/CD best practices.

Preferred Qualifications

• Knowledge of Prompt Engineering from a programmatic perspective (dynamic prompt templating).
• Familiarity with observability tools for AI, such as LangSmith or Arize Phoenix.