Backend Engineer - Python

Job Description

W-2 Open Positions Need to be Filled Immediately. Consultant must be on our company payroll, Corp-to-Corp (C2C) is not allowed.

Candidates encouraged to apply directly using this portal. We do not accept resumes from other company/ third-party recruiters -->

Job Overview

Job ID: J52785

Posted Date: 01/09/2026

--> Specialized Area: Python

--> Job Title: Backend Engineer - Python

Location: DALLAS, TX

Duration: 21 Months + Extension

Hourly Rate: Depending on Experience (DOE)

Domain Exposure: Healthcare, IT/Software

--> Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, L2-EAD, GC-EAD

Client: To Be Discussed Later

Employment Type: W-2, 1099, C2C Bench Recruiter: Natalie thomas

-->

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Full job description

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 .

• Apply Now