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Python + AI Lead

Pristine ResourceDallas, TX🇺🇸United StatesPosted 3 Sept 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to lead AI/ML initiatives, leveraging your extensive Python and AI experience to drive innovation. You'll thrive here if you're passionate about bringing cutting-edge AI systems to production and enjoy a collaborative environment. Apply today to shape the future of AI at Pristine Resource!

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Dallas, TX, United States
Posted
1 week ago
DockerFastAPIFlaskAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython

Job Description

Position: Python + AI Lead
Location: Dallas, TX (Hybrid)
Hire Type: W2 Only

Minimum Qualifications

  • ·       10+ Years of overall exp and 5+ 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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