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Python Developer / Architect with AI exp

CloudiousTX🇺🇸United StatesPosted 3 Sept 2026

Why This Role Stands Out

This role offers a fantastic opportunity to architect and develop cutting-edge AI-powered systems, leveraging your Python and LLM expertise. You'll thrive here if you're a seasoned developer with a passion for innovation and a desire to shape the future of AI solutions, with a hybrid work model offering flexibility.

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
TX, United States
Posted
6 days ago
DockerFastAPIFlaskAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython

Job Description

Python Architect / Developer with AI exp

Southlake, TX (4 days work from office)

3 months CTH or FTE

Minimum Qualifications

  • 10+ years of professional software development experience, with strong, current Python expertise.
  • Demonstrable experience delivering at least one AI/ML or LLM-powered system to production and supporting it through its life cycle.
  • Practical familiarity with the AI Development Life Cycle (AIDLC): data handling, experimentation, evaluation, deployment, and monitoring.
  • Hands-on experience with LLM application development - prompt engineering, RAG, and integrating APIs such as those from major model providers.
  • Proficiency designing and consuming RESTful APIs and building scalable backend services.
  • Working knowledge with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker).
  • Solid grounding with version control (Git) and CI/CD workflows.
  • Strong communication skills and the ability to work across engineering, data, and product teams.

Preferred Qualifications

  • Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or multi-agent frameworks).
  • Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS) and embedding-based retrieval.
  • Exposure to MLOps/LLMOps tooling - MLflow, Weights & Biases, model registries, and feature stores.
  • Experience with Kubernetes and infrastructure-as-code.
  • Understanding of model evaluation, responsible AI, safety guardrails, and observability for LLM systems.
  • Background with data pipelines and streaming (e.g., Kafka, Spark, or Airflow).

Core Tech Stack

Python FastAPI / Flask LLM & Agentic frameworks Vector databases Docker CI/CD Cloud (AWS / Google Cloud Platform / Azure) MLOps/LLMOps tooling

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