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Python + AI Lead

Lorven Technologies, Inc.Dallas, TX🇺🇸United StatesPosted Sep 21, 2026

Why This Role Stands Out

Lead cutting-edge AI projects at Lorven Technologies, Inc., leveraging your expertise in Python, LLMs, and RAG within a flexible hybrid environment. This role offers significant growth potential for experienced AI/ML professionals ready to make a substantial impact. Apply now to shape the future of AI development!

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Dallas, TX, United States
Posted
23 hours ago
DockerAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython

Job Description

Hi
Our client is looking for a Python + AI Lead project in Dallas, TX   below is the detailed requirement.
Job positing Title: Python + AI Lead
Location: Dallas, TX 
Required Skills: Python, AI/ML or LLM
 
Job description:
•    Bachelor’s degree in related field
•    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).

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