> Markdown version of [/jobs/ext/3783832-python-ai-lead](https://www.wearedevelopers.com/jobs/ext/3783832-python-ai-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Python + AI Lead

- **Company:** Cloudious LLC
- **Location:** Dallas, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Airflow, Amazon Web Services, Application Integration Architecture, Microsoft Azure, Cloud Computing, Encodings, Continuous Integration, Python (Programming Language), Software Engineering, Data Streaming, Pinecone, Feature Store, Data Processing, LangChain, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Model Validation, Llamaindex, Backend, Agentic-AI, Git, Fastapi, Pgvector, Containerization, Kubernetes, Weaviate, Apache Kafka, Machine Learning Operations, FAISS, Restful APIs, Software Version Control, Data Pipelines, Docker
- **Published:** October 16, 2026
- **Apply:** https://www.careerjet.com/jobad/usa88351b364cfce3d549edae2d1e3fad2

## About the Role

* 10+ Years of overall experience 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 in designing and consuming RESTful APIs and building scalable backend services.
 * Working knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker).
 * Solid grounding in version control (Git) and CI/CD workflows.
 * Strong communication skills and the ability to work across engineering, data, and product teams., * 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).
 * Python | FastAPI / Flask | LLM & Agentic frameworks | Vector databases | Docker | CI/CD | Cloud (AWS / GCP / Azure) | MLOps/LLMOps tooling