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Python + AI Lead

OrbITpeopleTX🇺🇸United StatesPosted 2 Sept 2026

Why This Role Stands Out

You'll lead cutting-edge AI development, driving innovation with Python and LLMs in a hybrid environment that offers flexibility and growth. This role is perfect for experienced AI/ML professionals passionate about building and deploying impactful solutions, and you're encouraged to apply to shape the future of AI at OrbITpeople.

Quick Overview

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

Job Description

Position: Python + AI Lead
Location: Dallas, TX (Hybrid)

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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